Data Science Manager Salary India Jobs

The Applied AI team designs and builds algorithmically driven features in the Datadog app. We work across a range of applications, primarily focusing on analysis on streaming data such as anomaly detection, error outliers and faulty deployment analysis. As an Applied Scientist you will work on building models and algorithms for machine learning powered features within the Datadog platform. You will work closely with our engineering and product partners to explore, build, scale and deliver these features that we incubate within the Applied AI team. At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: Design solutions for our different use cases. You will research and benchmark relevant algorithms to find the best fit for our use-cases Leverage machine learning algorithms and statistical techniques to build new scalable product features Develop, deploy and monitor new and existing features to production Participate in our journal club by reading and presenting the latest academic research papers to the team Explore, analyze and tell the story behind high volumes of data flowing through Datadog systems Maintain and monitor the models, services and infrastructure owned by your team Participate in your team’s on-call rotation Who You Are: You have a BS/MS/PhD in a Computer Science, Engineering, Machine Learning or related scientific field or equivalent experience You have experience working with high-scale systems and datasets including building models, applying machine learning to real business problems, and writing production data pipelines You can explain complex ideas and algorithms to non-technical audiences You care about code simplicity and performa

NVIDIA is leading groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU -- our invention -- serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables groundbreaking creativity and discovery, and powers inventions that were once considered science fiction, including artificial intelligence to autonomous cars. We are the GPU Communications Libraries and Networking team at NVIDIA. We build communication libraries like NCCL, NVSHMEM, and UCX that are crucial for scaling Deep Learning and HPC. We're seeking a Senior Software Architect to help co-design next-gen data center platforms and scalable communications software. DL and HPC applications have a huge compute demands and already run at scales of up to tens of thousands of GPUs. GPUs are connected with high-speed interconnects (e.g. NVLink, PCIe) within a node and with high-speed networking (e.g. InfiniBand, Ethernet) across nodes. Efficient and fast communication between GPUs directly impacts end-to-end application performance. This impact continues to grow with the increasing scale of next generation systems. This is an outstanding opportunity to advance the state-of-the-art, break performance barriers, and deliver platforms the world has never seen before. Are you ready to build the new and innovative technologies that will help realize NVIDIA's vision? What you will be doing: Investigate opportunities to improve communication performance by identifying bottlenecks in today's systems. Design and implement new communication technologies to accelerate AI and HPC workloads. Explore innovative solutions in HW and SW for our next generation platforms as part of co-design efforts involving GPU, Networking, and SW architects. Build proofs-of-concept, conduct experiments,

About the Team The Safety Systems team is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency. The Model Policy team aligns model behavior with desired human values and norms. We co-design policy with models and for models by driving rapid policy taxonomy iteration based on data and defining evaluation criteria for foundational models’ ability to reason about safety. Key focus areas include: catastrophic risk, mental health, teen safety and multimodal safety. About the Role Providing access to frontier AI systems raises complex questions around dual-use science and catastrophic risk. How should models respond to requests involving chemical synthesis, biological experimentation, or pathogen research? Where is the boundary between legitimate scientific inquiry and information that could enable misuse? How do we design policies that meaningfully reduce risk without unnecessarily restricting beneficial research? This is a senior role in which you’ll help shape policy creation and development at OpenAI for addressing biological and chemical risks. You will develop structured policy frameworks and taxonomies to guide safe model behavior. This role sits at the intersection of biosecurity expertise, AI safety research, and policy design. You will help ensure that frontier AI systems can support beneficial life sciences research, such as drug discovery, public health, and biosafety, while reducing the risk that these capabilities could be misused. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you’ll: Design and maintain model policies governing chemical and biological risk, defining how models should safely handle dual-use scenarios. Develop structured taxonomies of chemical and biological risk that inform model training data, evaluation benchmarks, and safet

About BlockTech BlockTech is an algorithmic trading firm operating at the frontier of global crypto derivatives and spot markets. We trade 24/7 across some of the fastest-moving, most data-rich venues in finance. Crypto remains one of the few markets where a researcher can still meaningfully move the edge: abundant data, novel microstructure, and the shortest possible loop between a research idea and live PnL. We're looking for an experienced Quantitative Researcher to take ownership of that edge and push it further. The role This is a senior, hands-on research seat on our trading floor. You'll own a research agenda end-to-end from hypothesis, dataset construction, feature engineering, model training, backtesting, live deployment, monitoring, and iteration. You'll be trusted to set its direction. You'll work shoulder-to-shoulder with fellow researchers, traders and analysts, shape how we price and trade, and help raise the bar for research across the floor, including mentoring less experienced researchers and influencing the tools and standards the team relies on. You will: Own price-prediction, signal, execution, and anomaly-detection models across crypto derivatives and spot markets from idea to live PnL using state-of-the-art ML Shape our research, backtesting, and trading infrastructure together with engineers, so good ideas reach production quickly and safely Own models in production: monitor live performance, diagnose decay, and iterate on what you ship Set research direction alongside traders, deciding which trades are worth making and why Raise the research bar by mentoring colleagues, reviewing work, and setting standards for rigour What we're looking for 4+ years of hands-on quantitative research and/or applied ML experience, with a track record of models you've taken into production trading live A strong academic foundation in a quantitative discipline (mathematics, physics, statistics, computer science, ML/AI, or similar) Fluency in Python and the modern

About BlockTech BlockTech is a fast-paced algorithmic trading firm at the frontier of global cryptocurrency derivatives and spot markets. We trade 24/7 across some of the most data-rich, fast-moving venues in finance, and we use that data to build smarter models, sharper signals, and more adaptive systems. Crypto is one of the few markets where a researcher can still meaningfully move the edge. The data is abundant, the microstructure is novel, and the feedback loop between a research idea and live PnL couldn’t be shorter. We’re growing fast, and we’re looking for a Quantitative Researcher with a strong machine learning toolkit to help us push that edge further. The role As a Quantitative Researcher on our trading floor, you’ll own ideas end-to-end from hypothesis and dataset construction through feature engineering, model training and backtesting, all the way to live deployment, monitoring, and iterative improvement. You’ll sit shoulder-to-shoulder with Quantitative Traders and Quantitative Analysts, and your work will directly drive how we price and trade. What you’ll work on Collaborating closely with traders to translate research insights into systematic trading strategies Designing, developing and deploying models for price prediction, signal generation, execution, and anomaly detection across crypto derivatives and spot markets using state-of-the-art AI and ML techniques. Building robust trading, backtesting and research infrastructure alongside our engineers, so promising ideas can move into production quickly and safely Owning models in production: monitoring live performance, diagnosing decay, and iterating on what you ship What we’re looking for A strong academic background in a quantitative discipline (Mathematics, Physics, Statistics, Computer Science, Econometrics, ML/AI, or similar), typically a PhD or an MSc with strong research experience Fluency in Python and the modern ML stack (PyTorch and/or TensorFlow, scikit-learn, NumPy, pandas) A deep, intuit

About BlockTech BlockTech is a fast-paced algorithmic trading firm at the frontier of global cryptocurrency derivatives and spot markets. We trade 24/7 across some of the most data-rich, fast-moving venues in finance, and we use that data to build smarter models, sharper signals, and more adaptive systems. Crypto is one of the few markets where a researcher can still meaningfully move the edge. The data is abundant, the microstructure is novel, and the feedback loop between a research idea and live PnL couldn’t be shorter. We’re growing fast, and we’re looking for a Quantitative Researcher with a strong machine learning toolkit to help us push that edge further. The role As a Quantitative Researcher on our trading floor, you’ll own ideas end-to-end from hypothesis and dataset construction through feature engineering, model training and backtesting, all the way to live deployment, monitoring, and iterative improvement. You’ll sit shoulder-to-shoulder with Quantitative Traders and Quantitative Analysts, and your work will directly drive how we price and trade. You will: Collaborate closely with traders to translate research insights into systematic trading strategies Design, develop and deploy models for price prediction, signal generation, execution, and anomaly detection across crypto derivatives and spot markets using state-of-the-art AI and ML techniques. Build robust trading, backtesting and research infrastructure alongside our engineers, so promising ideas can move into production quickly and safely Own models in production: monitoring live performance, diagnosing decay, and iterating on what you ship What we're looking for A strong academic background in a quantitative discipline (Mathematics, Physics, Statistics, Computer Science, Econometrics, ML/AI, or similar), typically a PhD or an MSc with strong research experience Fluency in Python and the modern ML stack (PyTorch and/or TensorFlow, scikit-learn, NumPy, pandas) A deep, intuitive grasp of overfitting, g

About BlockTech BlockTech is an algorithmic trading firm operating at the frontier of global crypto derivatives and spot markets. We trade 24/7 across some of the fastest-moving, most data-rich venues in finance. Crypto is one of the few markets where the gap between a good model and live PnL comes down to how fast and how reliably you can ship it. Abundant data, novel microstructure, and a short path to production mean the quality of our infrastructure directly moves the edge. We're looking for a Quantitative Developer to build that infrastructure and get our models into production. The role This is a hands-on engineering seat on our trading floor. You'll own the infrastructure that turns research models into production trading systems, working primarily in Python and Rust, the language our models and systems are written in. You'll build the frameworks and tooling that let quants ship their code to production and keep those systems running once they're live. You'll work shoulder-to-shoulder with researchers, traders, and fellow engineers, building the tooling, pipelines, and standards that let good ideas reach production quickly and safely, and helping raise the engineering bar across the floor. You will Build and own the infrastructure that takes models from research to production - data pipelines, backtesting, deployment, and monitoring. Turn research prototypes into robust, performant, production-grade code that runs reliably in live trading Work closely with quantitative researchers and traders to get new models live quickly and safely Own the systems you ship in production: monitor performance, diagnose issues, and keep latency and reliability high Improve the tools, standards, and pipelines the team relies on, raising the engineering bar across the floor What we're looking for 2+ years of software engineering experience, with a track record of shipping production-grade systems A strong academic foundation in a STEM discipline (computer science, mathematics, p

About BlockTech BlockTech is an algorithmic trading firm operating at the frontier of global crypto derivatives and spot markets. We trade 24/7 across some of the fastest-moving, most data-rich venues in finance. Crypto remains one of the few markets where a researcher can still meaningfully move the edge: abundant data, novel microstructure, and the shortest possible loop between a research idea and live PnL. We're looking for an experienced Quantitative Researcher to take ownership of that edge and push it further. The role This is a senior, hands-on research seat on our trading floor. You'll own a research agenda end-to-end from hypothesis, dataset construction, feature engineering, model training, backtesting, live deployment, monitoring, and iteration. You'll be trusted to set its direction. You'll work shoulder-to-shoulder with fellow researchers, traders and analysts, shape how we price and trade, and help raise the bar for research across the floor, including mentoring less experienced researchers and influencing the tools and standards the team relies on. What you'll do Own price-prediction, signal, execution, and anomaly-detection models across crypto derivatives and spot markets from idea to live PnL using state-of-the-art ML Shape our research, backtesting, and trading infrastructure together with engineers, so good ideas reach production quickly and safely Own models in production: monitor live performance, diagnose decay, and iterate on what you ship Set research direction alongside traders, deciding which trades are worth making and why Raise the research bar by mentoring colleagues, reviewing work, and setting standards for rigour What we're looking for 4+ years of hands-on quantitative research and/or applied ML experience, with a track record of models you've taken into production trading live A strong academic foundation in a quantitative discipline (mathematics, physics, statistics, computer science, ML/AI, or similar) Fluency in Python and the m

We’re looking for Software Engineering Interns to help build and scale the systems that power Datadog’s observability and security platform. Interns contribute directly to real-world engineering challenges across backend, frontend, infrastructure, data engineering, and developer tooling while working alongside experienced engineers and mentors. You’ll help design, build, and improve systems that process and analyze massive volumes of metrics, logs, and application data in real time. Whether you’re interested in distributed systems, Kubernetes, AI-powered products like Bits AI, or developer platform tooling, you’ll work on meaningful projects that deliver impact to customers at global scale. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Contribute to production systems that process and analyze large-scale observability and application data in real time Build and improve distributed systems across backend infrastructure, developer platforms, and cloud-native services Help identify and solve performance, reliability, and scalability challenges in critical services supporting Datadog’s growing customer base Own and deliver technical projects from design through deployment with support from experienced engineers and mentors Develop technical expertise through hands-on experience with technologies such as Kubernetes, distributed systems, and cloud-native infrastructure Collaborate with fellow interns, mentors, and engineers while building software that delivers impact at global scale Who You Are: Pursuing a degree in Computer Science, Software Engineering, or a related technical field, or have equivalent practical experience Targeting a 2028 full-time start date Demonstrate strong computer science fundamentals, including data struc

Please note that the job is only available from the locations outlined. We are looking for a Senior Software Engineer to help us take REDAPL, our Referential Data Platform, to the next level. REDAPL is Datadog's main platform for tracking our customers' infrastructure resources and relationships. The platform enables products where customers can understand, keep track of, and gain insights into their infrastructure related to performance, cost, security, and more. Many Datadog products use REDAPL today such Cloud Security Posture Management, Resource Catalog, Cloud Cost Management, and Service Catalog and others - REDAPL ingests more than 4.5mil updates/second. As a Senior Engineer, you will drive, lead and collaborate on projects both inside and outside the platform. You can expect to contribute to key technical decisions relating to our data ingestion, processing, and query pipelines. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Build a query engine that supports efficient relationship traversals for our most demanding workloads. Contribute to design and drive high-priority, high-visibility projects to increase the platform's value, resilience, and scalability across multiple teams. Lead and guide other engineers through architectural platform decisions Identify potential system risks and trends in reliability and design solutions to address them Provide input on prioritizing engineering-led initiatives in short- and long-term planning and roadmaps Collaborate with internal product teams to understand their requirements and how we plan for their product growth as they integrate and depend on REDAPL Who You Are: You have a BS/MS/PhD in a Computer Science, Engineering or related scientific field or equivalent experience You

The Applied AI team designs and builds algorithmically driven features in the Datadog app. We work across a range of applications, primarily focusing on analysis on streaming data such as anomaly detection , error outliers and faulty deployment analysis . As an Applied Scientist you will work on building models and algorithms for machine learning powered features within the Datadog platform. You will work closely with our engineering and product partners to explore, build, scale and deliver these features that we incubate within the Applied AI team. At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: Design solutions for our different use cases. You will research and benchmark relevant algorithms to find the best fit for our use-cases Leverage machine learning algorithms and statistical techniques to build new scalable product features Develop, deploy and monitor new and existing features to production Participate in our journal club by reading and presenting the latest academic research papers to the team Explore, analyze and tell the story behind high volumes of data flowing through Datadog systems Maintain and monitor the models, services and infrastructure owned by your team Participate in your team’s on-call rotation Who You Are: You have a BS/MS/PhD in a Computer Science, Engineering, Machine Learning or related scientific field or equivalent experience You have experience working with high-scale systems and datasets including building models, applying machine learning to real business problems, and writing production data pipelines You can explain complex ideas and algorithms to non-technical audiences You care about code simplicity and performance You are excited to work on

Join the MongoDB Server Query Optimization team, and help us build a world-class distributed open-source query optimizer. Our team plays a crucial role in the experience and performance of data processing. We are responsible for the MongoDB Query Language and the lifecycle of each query, through parsing, optimization and plan selection. We have a presence across the US and Europe including New York, Dublin, Seattle, Palo Alto, and Chicago. We support office-based and remote work and align projects with convenient work hours for each time zone. We have tons of interesting problems to solve with a direct impact on users for transactional, time-series, and analytical workloads. The team is endeavoring to systematically rewrite every major component of our optimization and execution systems. We need your help to design and build the heart of a distributed, flexible schema, document database. ​​This role can be based out of our US offices or remotely in the North America region. Candidate Profile 10+ years of experience in data management systems, distributed systems, or large-scale backend engineering Experience with building production-level code with a large user base, robust design structure and rigorous code quality Degree in Computer Science or similar field, or equivalent practical experience, with strong competencies in data structures, algorithms, and software design/architecture Experience with large code bases written in C++ or another systems programming language. You'll need to trace down defects, estimate work complexity, and design evolution and integration strategies as we rewrite different components of the system A strong foundation in core database internals is essential. While direct experience in query optimization is a massive bonus, it is not a prerequisite. We are also excited to meet candidates with strong backgrounds in compilers, language transpilers, or distributed storage systems Position Expectations Innovate in the area of flexible schema d

Join the MongoDB Server Query Execution team, and help us build a world-class distributed open-source database. Our team plays a crucial role in the performance and efficiency of MongoDB's data processing. We are responsible for building and improving the core execution engine that powers all queries, taking a logical query plan produced by the optimizer and turning it into reality. This includes developing the physical operators for data retrieval and manipulation, improving the runtime for complex analytical and transactional workloads, and owning critical components such as our new execution engine. In addition to the core server, we support the query execution needs of other major products like Atlas Streams, Atlas Search and Vector Search, and mongosync, making our work vital to the entire MongoDB ecosystem. You will be joining a globally distributed team with a significant presence in both North America and Europe. While this role is based in the NAMER region, you will regularly collaborate closely with colleagues across different time zones. We support both office-based work in our North America hubs like New York, as well as remote work. We have tons of interesting problems to solve with a direct impact on users for transactional, time-series, and analytical workloads. To meet the ever-increasing data demands of modern applications, we are actively evolving our query system; this includes strategically re-architecting and improving key components of our query execution engine. We need your help to design and build the core of a distributed, flexible schema document database. This role can be based out of one of our North America offices, such as NYC or Palo Alto, or remotely across North America. Candidate Profile 10+ years of hands-on, professional experience in query engine development or database internals Experience with building production-level code with a large user base, robust design structure and rigorous code quality Degree in Computer Science or

Join the MongoDB Server Query team, and help us build a world-class distributed open source query engine. Our team plays a crucial role in the experience and performance of data processing. We are responsible for the MongoDB Query Language and the lifecycle of each query, from parsing to optimization to plan selection and finally execution. This also includes our geospatial search and update subsystems. Our global team is growing fast. In North America, we have a presence across the US and Canada including New York, West Coast, Toronto. In Europe, we have a presence in Dublin, Germany, France, Netherlands, UK, Bulgaria, Spain and Italy currently. We support office-based and remote work and align projects with convenient work hours for each time-zone. We have tons of interesting problems to solve with direct impact on users for transactional, time-series and analytical workloads. We need your help to design and build the heart of a distributed, flexible schema, document database. We are looking to speak to candidates who are based in Dublin or Cork for our hybrid working model. Candidate Profile 3+ years of experience in data intensive environments Hands-on experience building industrial-strength software Solid computer science fundamentals, with strong competencies in data structures, algorithms, and software design/architecture Experience with large code bases B.Sc in Computer Science or similar field, or equivalent practical experience Experience in C++ and in developing database systems is a plus Interest in the theory and practice of database query engines. Hands-on experience or M.Sc./Ph.D in the domain is a plus Position Expectations Understand and improve current functionality of the MongoDB query engine Identify, design, implement, test, and support new features related to query performance and robustness, query language enhancements, diagnostics for query performance problems, and integration with other products and tools Work with other engineers to

About Voyage AI Team at MongoDB Voyage AI team in MongoDB is building a best-in-class, general-purpose, domain-specific, and fine-tuned embedding models and rerankers to enable accurate, efficient unstructured data search and retrieval for RAG, recommendation, semantic search, and more. It is backed by a strong team of AI researchers from Stanford, MIT, Berkeley, Princeton, and CMU, who have conducted over five years of cutting-edge research on training embedding models. Voyage AI was acquired by MongoDB recently, and is now integrating the SOTA embedding models with MongoDB's data platform to create powerful end-to-end solutions. We are looking to speak to candidates who are based in Palo Alto for our hybrid working model. Position Overview We are seeking a Staff Research Scientist to join our team and contribute to the development of next-generation AI models. This position offers a unique opportunity to work on challenging problems at the intersection of machine learning research and practical deployment of large neural networks. This role can be based out of our Palo Alto office, or remotely in the United States. Responsibilities Conduct cutting-edge research in artificial intelligence, from frontier LLMs to embedding models and rerankers Innovate in next-generation information retrieval and LLM agent paradigm Collaborate closely with other research scientists and research engineers as well as peers across the organization Qualifications PhD degree in Computer Science or related field A track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications in top venues Strong background in machine learning, deep learning, and natural language processing Experience building complex neural networks for language and visual understanding Capable of conducting rigorous empirical studies to validate theoretical results Excellent leadership, problem-solving, and communication ski