Data Science Manager Salary India Jobs

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. Position Summary The growing DSNP business has created an opportunity for an individual with claims experience, who is familiar with the 837 standard claims format. This individual will own 837 file transmissions to the states, manage and act on state response files, ensuring that all transactions transmitted are complete and error free. Manage and complete error corrections to meet state requirements. Required Qualifications 1&#43; year of experience with encounter data, medical claims, or Medicare/Medicaid. 1&#43; year of experience using FTP and data transfer software. 1&#43; year of data management experience. Preferred Qualifications Experience with Microsoft Access Databases. Analytical skills with the ability to identify and resolve data discrepancies. Working knowledge of the 837 claims files. Education Bachelors degree or equivalent work experience Anticipated Weekly Hours 40 Time Type Full time Pay Range <p s

What if the work you did every day could impact the lives of people you know? Or all of humanity? At Illumina, we are expanding access to genomic technology to realize health equity for billions of people around the world. Our efforts enable life-changing discoveries that are transforming human health through the early detection and diagnosis of diseases and new treatment options for patients. Working at Illumina means being part of something bigger than yourself. Every person, in every role, has the opportunity to make a difference. Surrounded by extraordinary people, inspiring leaders, and world changing projects, you will do more and become more than you ever thought possible. Summary The Staff Data Engineer is a seasoned, hands-on engineer who designs, builds, and scales data products on our cloud lakehouse, powering analytics, reporting, and AI/ML across Illumina. We are looking for someone with strong proficiency in Python, SQL, and data modeling, a solid understanding of distributed systems and system design who has built and scaled data products on modern cloud platforms such as Databricks and Snowflake. This is a hands-on, senior individual-contributor role with end-to-end ownership and leadership spanning multiple domains such as Supply Chain, Manufacturing and Quality, including mentoring engineers on our global (India-based) team. Responsibilities Partner across business, AI, and platform teams translating domain needs (e.g., SAP, Manufacturing, Quality) into well-modeled, governed and scalable data products. Design, build, and scale end-to-end data products on Databricks (and interoperating with Snowflake) — from ingestion through curated, analytics-ready datasets following a medallion (Bronze/Silver/Gold) architecture. Develop reusable frameworks, libraries, and

Join us in building the future of finance. Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading. About the team + role We are building an elite team, applying frontier technologies to the world's biggest financial problems. We're looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn't a place for complacency, it's where ambitious people do the best work of their careers. We're a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards. Our Core Data Engineering team is responsible for designing and building all foundational datasets used across Robinhood and within our operational business areas. We build the foundational data models and core data layers that are consumed by downstream users, ensuring high data quality and reliability. We also develop internal data and AI tooling that enables teams across the company to scale their data development workflows efficiently. Our team collaborates with engineering, product, brokerage, crypto, and marketing teams to expand and grow Robinhood's products around the world ! We also partner with Machine Learning teams to build robust training datasets that power intelligent product features. As the Engineering Manager for our Toronto Data Engineering team, you will lead a team of exceptional engineers and drive the execution of key data initiatives. In this role, you will balance technical leadership with people management, dedicating approximately 60% of your time coaching and 40% to hands-on technical contributions, such as code and architecture reviews. You will drive roadmap planning and establish clear goals for the team, particularly as we expand into new mark

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. Position Summary Designs, builds, and maintains large-scale data infrastructure and data processing systems. Implements robust and scalable solutions to support data-driven applications, analytics, and business intelligence. What you will do Ensures seamless integration of data from different sources, such as databases, application programming interfaces (APIs), or streaming platforms. Optimizes data processing and query performance by fine-tuning data pipelines, database configurations, and data partitioning strategies. Establishes data quality checks and validations to identify and resolve data issues, ensuring high-quality and reliable data for downstream applications and analytics. Implements security measures to protect sensitive data throughout the data lifecycle by working closely with security teams to ensure data encryption, access controls, and compliance with data protection regulations. Collaborates with cross-functional teams, including data scientists, analysts, software engineers, and business stakeholders. Designs and develops data infrastructure, including data warehouses, data lakes, and data pipelines. Establishes auditing and monitoring mechanisms to track data access and maintain data governance standards. Establishes monitoring and alerting mechanisms

At Freddie Mac, our mission of Making Home Possible is what motivates us, and it’s at the core of everything we do. Since our charter in 1970, we have made home possible for more than 90 million families across the country. Join an organization where your work contributes to a greater purpose. Position Overview: The Cyber Security team at Freddie Mac is searching for a strong data analyst to collaborate on developing and administering data security policies as well as safeguarding information, evaluating existing data security procedures and identifying new areas of risk for Freddie Mac. If this role sounds like a fit for your skill set, please read on, apply and learn why there is #MoreatFreddieMac ! Our Impact: We develop and train the enterprise on relevant Identity and Access Management best practices, develop and support automated IAM processes, and develop and implement ongoing IAM efficiency improvements with limited oversight by managers. The role will work closely with Enterprise partners and security control owners on IAM automation development activities and alignment of IAM processes with InfoSec maturity targets as described in the InfoSec Strategy. Your Impact: Senior Data Analyst who can develop and implement new and updated business process automation for all Identity and Access Management activities. Develop and proposes strategies to reduce security risk in the organization by implementing procedural prevention, detection, and response measures, while still enabling positive business outcomes. Comfortable supporting ad hoc data retrieval and analysis requests using SQL queries and Copilot/Excel . Creating audit-ready reference documentation. This role will allow for and support rapid AI-based extrapolation/replication of these services as future automated IAM microservices. Qualifications: Typically, 5 - 7 years of rel

Principal Data Privacy Architect Description - Job Summary - Role Purpose • Lead and oversee complex, cross-functional privacy and data protection programs from strategy through implementation, ensuring alignment across business, technical, legal, and compliance stakeholders. • This role will design and implement scalable, AI-ready data privacy architecture across enterprise data environments, applications, and AI-enabled workflows. • The Principal Data Privacy Architect will serve as a hands-on subject matter expert responsible for embedding privacy-by-design, consent enforcement, data sovereignty, data loss prevention, and compliance controls into large, complex global data environments. • The architect will partner closely with Data Engineering, Cybersecurity, Legal, Privacy, AI Governance, Product, and Enterprise Architecture teams to ensure customer, employee, partner, and sensitive enterprise data is accessed, processed, shared, retained, and protected in a compliant, secure, and trustworthy manner. - Why This Role Matters • Architect for Trust & Scale: Build reusable privacy architecture patterns that enable secure, compliant, and scalable data usage across platforms, products, and regions. • Enable Responsible AI: Design privacy guardrails for AI agents, generative AI, RAG pipelines, model inputs and outputs, embeddings, vector stores, and automated data workflows. • Reduce Risk While Enabling Innovation: Translate privacy, consent, regulatory, and data sovereignty obligations into practical engineering controls that accelerate business outcomes. Responsibilities - Think Customer First • Embed customer trust, transparency, and privacy-by-design principles into enterprise data platforms and customer-facing applications. • Design consent-aware data access and usage p

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 122,000 colleagues serve people in more than 160 countries. JOB DESCRIPTION: Position Overview The Principal Data Engineer (IC) is a senior individual contributor and the accountable technical leader for assigned cross-domain initiatives and enterprise data engineering capabilities. The role owns integrated technical direction and technical outcomes for work spanning multiple data domains, defines and stewards enterprise engineering standards and reference architectures, and drives convergence where duplicated or inconsistent solutions create enterprise cost, risk, or operational burden. The role advises on scope, sequencing, capacity, dependencies, and technical debt, but does not independently commit domain resources or business delivery dates. This position has no people-management responsibility. Enterprise Data operates a domain-aligned model built on Databricks and Unity Catalog. Working with Domain Leaders, Staff Engineers, Platform Engineering, and partner organizations, the role converts ambiguous enterprise needs into executable architecture and carries the most complex or highest-risk work through validation and production. The role remains hands-on through prototyping, reference implementations, critical-path development, design and code review, and production problem solving. This role is based in Madison, WI. Essential Duties Include, but are not limited to, the following: Cross-domain technical leadership and delivery Own the technical outcome of assigned cross-domain initiatives from initial ambigu

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 122,000 colleagues serve people in more than 160 countries. JOB DESCRIPTION: The Staff Data Engineer is a senior, hands-on technical contributor within an Enterprise Data domain, owning technical design and the most complex implementation work for assigned data products, capabilities, and integrations. The role establishes, communicates, and evolves the technical approach for the work it leads and is accountable for the quality, durability, and supportability of the solutions it shapes. Staff Data Engineers work directly with business stakeholders to understand needs and shape technical solutions, engaging at a level appropriate to the work they lead. The role is expected to be fluent in both technical execution and business context, translating between them without losing precision in either. The Staff Data Engineer sets technical direction for assigned capabilities and initiatives within the domain, applies enterprise standards and platform patterns, engages Principal Engineers where cross-domain considerations apply, and multiplies the effectiveness of the domain team through design leadership, code review, and mentoring. This role is based in Madison, WI . Essential Duties Include, but are not limited to, the following: Technical design and solutioning Own technical design and hands-on delivery for the most complex or highest-risk work across assigned data products, capabilities, and initiatives, including data models, pipeline architecture, integration patterns, and platform usage decisions. Produce design docume

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team The Data Product Experiences (DPE) team is dedicated to making Stripe's merchant-facing data products intuitive, delightful, and actionable. We build client-side rendering engines, high-performance charting libraries, and interactive visualization interfaces that handle high-throughput streaming telemetry and real-time merchant analytics. What you’ll do In this role, you will architect and build high-performance client-side data rendering engines from scratch to visualize tens of thousands of data points smoothly. You will collaborate closely with Designers, Product Managers, and Front-End Systems Engineers to define cutting-edge data visualization paradigms that reduce cognitive load and prevent visual fatigue for Stripe's users. Responsibilities Partner with designers and product managers to prototype, build, and ship interactive analytics dashboards and data visualization tools across the Stripe Dashboard. Contribute to Stripe’s core design system by developing scalable, reusable visualization components and charting standards for engineering teams. Design frontend integration strategies and robust API contracts to enable seamless adoption of analytics components by partner teams. Provide technical leadership and mentorship through code reviews, pairing, and fostering frontend engineering best practices. Collaborate across Product, Design, Data Infrastructure, and Analytics teams to deliver high-quality, data-driven user experiences. Opti

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team The Data Product Experiences (DPE) team is dedicated to making Stripe's merchant-facing data products intuitive, delightful, and actionable. We build client-side rendering engines, high-performance charting libraries, and interactive visualization interfaces that handle high-throughput streaming telemetry and real-time merchant analytics. What you’ll do In this role, you will architect and build high-performance client-side data rendering engines from scratch to visualize tens of thousands of data points smoothly. You will collaborate closely with Designers, Product Managers, and Front-End Systems Engineers to define cutting-edge data visualization paradigms that reduce cognitive load and prevent visual fatigue for Stripe's users. Responsibilities Partner with designers and product managers to prototype, build, and ship interactive analytics dashboards and data visualization tools across the Stripe Dashboard. Contribute to Stripe’s core design system by developing scalable, reusable visualization components and charting standards for engineering teams. Design frontend integration strategies and robust API contracts to enable seamless adoption of analytics components by partner teams. Provide technical leadership and mentorship through code reviews, pairing, and fostering frontend engineering best practices. Collaborate across Product, Design, Data Infrastructure, and Analytics teams to deliver high-quality, data-driven user experiences. Opti

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. POSITION SUMMARY CVS Health is seeking a highly skilled Senior Data Engineer, Observability Engineering to join the Enterprise Observability Platform organization and help advance the next generation of observability, infrastructure, and security data capabilities. The Senior Data Engineer, Observability Engineering will play a critical role in designing, building, and operating scalable data pipelines and data products that power enterprise observability, operational intelligence, and security analytics across the organization. The Senior Data Engineer, Observability Engineering is a senior individual contributor responsible for developing and optimizing Databricks-based data engineering solutions that ingest, transform, govern, and deliver high-volume telemetry, infrastructure, application, and security data. This role combines deep hands-on technical execution with ownership of engineering excellence, operational reliability, performance optimization, and data platform best practices. Working closely with Observability Engineering, Security Engineering, Infrastructure Engineering, and Data Platform teams, the Senior Data Engineer, Observability Engineering will contribute to the evolution of the enterprise observability lakehouse by building resilient ingestion frameworks, establishing data quality standards, enhancing governance controls, and driving efficient, scalable data processing patter

NVIDIA’s DGX Cloud organization is seeking a Senior Data Engineer to become part of its data team! We develop the reliable data foundation that supports fleet health, capacity, utilization, cost, reliability, and operational decision-making throughout DGX Cloud. Our platform supports engineering, operations, finance, and product teams managing and expanding large GPU fleets across cloud service providers and NVIDIA Cloud Partners. We are looking for a practical engineer and technical lead to take charge of a key part of the Navigator data platform. We develop the systems that transform distributed infrastructure telemetry and operational data into dependable, managed data products that support fleet health, capacity, utilization, cost, and operational decisions. We are seeking a hands-on, platform-minded engineer to build and evolve the systems that turn distributed infrastructure telemetry and operational data into reliable, governed data products. You will work across ingestion, transformation, data quality, platform architecture, security, observability, and self-service consumption to help make Navigator and the DGXC data platform a dependable source of truth. We do expect strong engineering fundamentals, experience operating production systems, and the ability to learn new platforms and domains quickly. What you'll be doing: Own systems end to end. For example, work from ambiguous customer and operational needs through architecture, implementation, deployment, observability, incident response, and ongoing support. Construct data pipelines and products. Such as designing and maintain batch and streaming ingestion, transformation, reconciliation, and serving paths for fleet, capacity, utilization, cost, scheduling, and operational telemetry. Build shared libraries, workflow and DAG or equivalent experience abstractions to evolve the data platform. Develop deployment tooling, data

Our Purpose Mastercard powers economies and empowers people in 200&#43; countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Senior Data Scientist-2 Overview Product Data & Analytics is a centralized global team that enables Mastercard product business units to make better data‑driven decisions. We build internal analytics partnerships that strengthen focus on business performance, portfolio and revenue optimization, initiative tracking, new product development, and go‑to‑market strategies. These capabilities are underpinned by our data platforms that enable insights to be delivered in a standardized, scalable, and cost‑efficient manner. Key Responsibilities Strategic Support • Design and implement value enablement frameworks that optimize pricing strategies, enhance pre-sales propositions, and ensure customer success. • Collaborate with global and regional teams to tailor solutions that meet regional business needs and align with Mastercard's objectives. • Provide data-driven insights and recommendations to optimize pricing, pre-sales strategies, and customer success outcomes. • Develop frameworks, project structures, and presentations to communicate key strategic initiatives. • Conduct data integrity checks and ensure quality and reliability in data used for analysis. • Translate complex business problems into analytical solutions that support strategic decision-making. Technical Leadersh

About the Role: We're hiring Senior and Staff Data Platform Engineers to join the Data Infrastructure teams in Toronto. Together these teams own the infrastructure that processes billions of events per day: Spark-on-Kubernetes, Flink and Kinesis pipelines, a multi-petabyte Delta Lake, a large-scale MemoryDB feature store, Databricks multi-environment operations, and the catalog and lifecycle systems that govern it. The team is small and senior. Each engineer owns major platform components: you design it, build it, and support it in production. This is a hybrid-role based out of our Toronto office. You must be willing to travel to our Toronto office two days/week. What You'll Do: Spark-on-Kubernetes — EKS-based compute platform for Spark workloads: cluster configuration, Pod Identity IAM, job environment setup, Kustomize overlays, and shadow canary validation Event ingestion — Rust services and Flink jobs processing billions of events per day over Kinesis; throughput, reliability, on-call response, and AI-assisted operational tooling to reduce toil Platform infrastructure — Terraform modules for environment provisioning, cross-account AWS IAM, ARC runner infrastructure, and CI/CD for data platform changes Feature store and ML compute — Flink-based real-time feature pipelines feeding a large-scale MemoryDB cluster; GPU capacity governance and Databricks multi-environment operations for ML training workloads Workflow orchestration and CDC — Airflow-based DAG deployment, change data capture pipeline operations, and data quality monitoring Your Background: 3+ years building and operating production data platform infrastructure at the cluster or platform level, across Spark, Flink, Kinesis, Kubernetes, or equivalent Deep experience in at least one of: Spark-on-K8s cluster operations, Rust-based data or systems engineering, Kubernetes platform engineering and IaC, or data catalog and governance tooling Production AWS experience or equivalent: EKS, S3, Kinesis, and mu

We're looking for an ML Data & Platform Engineer to own the infrastructure that powers our speech AI models: the pipelines that source and prepare training data, and the platform that trains, evaluates, and serves them in production. Speech AI has a data problem most ML teams don't, and you'll be at the centre of solving it, working as part of our ML team to remove friction across the entire lifecycle and get better models into production faster. This is a broad, cross-functional role suited to someone who enjoys working across the full stack: data infrastructure, distributed systems, and production ML, and who takes ownership of problems end to end rather than waiting to be told what to fix. What you'll do Designing, building, and maintaining scalable data pipelines for ingesting, transforming, validating, and storing large datasets used to train our models Developing and maintaining web scraping and data acquisition solutions to keep training datasets fresh, high-quality, and available at scale Building and operating the infrastructure that lets the ML team deploy and evaluate new models quickly, and that serves models efficiently and reliably in production Optimising infrastructure for both iteration speed and production reliability, including GPU utilisation, job scheduling, and training efficiency Implementing observability (monitoring, logging, alerting) across data pipelines and ML systems to catch issues early and keep things running smoothly Troubleshooting complex issues across distributed systems, spanning data infrastructure, training, and inference Continuously improving our data and MLOps practices, and helping shape the roadmap for how our platform evolves as we scale What you'll need Strong proficiency in Python and SQL, with a solid backend or data engineering foundation Hands-on experience with containerisation and orchestration (Docker, Kubernetes), and working with a major cloud provider Experience building data pipelines and ETL/ELT processe