Decision Scientist Jobs

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. As a Data Scientist in the Pay, Integrity & Identity org, you will collaborate with our world class team of engineers, product managers, analysts and other data scientists to help create best in class pay platforms, stop fraudulent actors from harming our riders & drivers fraud and build user trust on the Lyft platform. You will run experiments (A/B tests) and develop data driven solutions to launch new features and remove the bad actors from the Lyft platform while maintaining a positive experience for genuine users. We’re looking for an intellectually curious individual who has extraordinary attention to detail, a track record of analytical problem-solving and skilled communication. Prior experience in the fintech, fraud or identity space is preferred. Responsibilities: Design and analyze experiments in collaboration with other scientists, product & engineering; communicate findings to stakeholders and facilitate launch decisions Leverage advanced statistical techniques to generate quantitative insights and develop machine learning models Analyze the wide variety of signals available to identify patterns in large datasets and uncover root causes Partner with product managers, engineers, and operators to translate analytical insights into decisions and action Build data pipelines and develop analytical frameworks to monitor business and product performance Set business metrics that measure the health of our products, as well as passenger and driver experience Collaborate with product and engineering and communicate findings to stakeholders in a clear and concise manner Experience: Degree in a quantitative field such as statistics, economics, applied math, operations research or engineering (advanced degrees preferred), or relevant work experience 4-6+ years of industry experi

We believe communication belongs to everyone. We exist to democratize phone service. TextNow is evolving the way the world connects, and that's because we're made up of people with curious minds who bring an optimistic yet critical lens into the work we do. We're the largest provider of free phone service in the nation. And we're just getting started. Join us in our mission to break down barriers to communication and free the flow of conversation for people everywhere. About the role We’re looking for a business-minded Data Scientist who pairs strong technical skills with sound judgment about which problems are worth solving. You’ll partner with product, marketing, finance, engineering, and trust & safety to turn ambiguous questions into rigorous analyses, models, and experiments — and then turn the results into decisions. The problems are real and the data is large: understanding what drives user acquisition, engagement, and retention; improving monetization across ads and subscriptions; measuring the impact of product changes; and helping keep our network safe from fraud and abuse. You’ll own work end to end, from framing the question to landing the recommendation with leadership. What you’ll do Frame the right questions. Work with stakeholders to understand business challenges, spot where data can make a difference, and translate open-ended questions into structured analyses with clear success criteria. Find what drives the business. Dig into large, complex datasets to uncover the trends, segments, and behaviors behind user growth, engagement, retention, and revenue. Build models that get used. Develop and apply statistical and machine learning methods — predictive models, segmentation, forecasting, causal inference — and evaluate them honestly, including their assumptions and limitations. Run and read experiments. Design, analyze, and interpret A/B tests and quasi-experiments that inform product and marketing decisions. Defin

Use Your Power for Purpose At Pfizer, our purpose is to deliver breakthroughs that change patients’ lives. In this role, you will contribute to the discovery and advancement of innovative medicines for patients worldwide by applying rigorous science, modern pharmacology, and cutting-edge technologies. Your work will help transform promising therapeutic ideas into high-quality experimental data that enable confident decisions across the drug discovery process. What You Will Achieve A key strategic focus within the Internal Medicine Research Unit (IMRU) is the discovery of novel therapies for obesity, cardiovascular disease, and related cardiometabolic conditions. As an Associate Scientist in the In Vitro Pharmacology group, you will contribute to early drug discovery programs by generating rigorous, reproducible pharmacology data that support target validation, assay development, compound profiling, and mechanism-of-action studies. This role is well suited for a collaborative, hands-on scientist with experience in cell-based assay execution, mammalian cell culture, quantitative data analysis, and an interest in receptor pharmacology within cardiometabolic drug discovery. How You Will Achieve It Develop, optimize, and execute cell-based signaling, ligand-binding, and other in vitro pharmacology assays to generate high-quality data that support project decisions. Support compound profiling and mechanism-of-action studies through careful experimental design, troubleshooting, and quantitative data analysis. Maintain mammalian cell cultures and prepare fit-for-purpose cellular or membrane-based assay reagents to support screening, binding, and functional assay workflows. Apply core pharmacology concepts, including ligand potency, efficacy and affinity to interpret experimental results. Work collaboratively with pharmacology colleagues and cross-functional pr

What You Will Achieve As a Senior Scientist, you will be at the center of our operations and you’ll find that everything we do, every day, is in line with an unwavering commitment to quality. You will be recognized as a technical expert and a scientific contributor. With your deep knowledge of the discipline, you will be an active team member whose decisions impact the projects. You will perform qualitative and quantitative analyses of organic and inorganic compounds to determine chemical and physical properties during chemical syntheses, or drug product development process. You will be using your scientific judgment to adapt standard methods and techniques by applying prior work experience. You will be forecasting and planning resource requirements for your project team. Your creativity in developing novel processes and new ideas will be used frequently. You will undertake mentoring activities to guide team members. It is your innovative scientific temperament that will help in making Pfizer ready to achieve new milestones and help patients across the globe. How You Will Achieve It This colleague will be responsible for developing analytical strategies in support of pharmaceutical drug substance and/or drug products during all development phases, including supporting manufacturing process development, developing, validating and transferring analytical methods, designing stability studies for shelf life assignments, and developing impurity control strategies. Collaborates with colleagues and subject matter experts to assess the most appropriate analytical approach to support project activities, including use of computational predictive tools, modelling software and data visualization tools where appropriate. Perform lab work and delegate responsibilities and review peer lab work as appropriate, Through effective communicat

Scale Labs, Research Scientist — Agent Robustness As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities. Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision. As a Research Scientist working on Agent Robustness you will work on the fundamental challenges of building AI agents that are safe and aligned with humans. For example, you might: Research the science of AI agent capabilities with a focus on how they relate to safety, risk factors, and methodologies for benchmarking them; Design and build harnesses to test AI agents’ tendency to take harmful actions when pressured to do so by users or tricked into doing so by elements of their environment; Design and build exploits and mitigations for new and unique failure modes that arise as AI agents gain affordances like coding, web browsing, and computer use; Characterize and design mitigations for potential failure modes or broader risks of systems involving multiple interacting AI agents. Ideally you’d have: Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance. Practical experience conducting technical research collaboratively. You should be comfortable building and leveraging agent scaffolding, designing evaluation harnesses, an

Scale Labs, Research Scientist — AI Controls and Monitoring As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities. Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision. As a Research Scientist focused on AI Controls and Monitoring, you will design methods, systems, and experiments to ensure that advanced AI models and agents remain aligned with intended goals, even in high-stakes or adversarial environments. For example, you might: Develop monitoring techniques and observability methods that track AI behavior in real time to identify and flag deviations, emergent capabilities, or anomalous outputs; Research mechanisms for layered control, including fail-safes, oversight protocols, and intervention methods that can halt or redirect AI systems when risks are detected; Design red-team simulations to probe weaknesses in oversight and control mechanisms, and build mitigations to close identified gaps; Collaborate with policymakers, engineers, and other researchers to establish standards and benchmarks for AI monitoring and escalation. Ideally you’d have: Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance. Practical experience conducting technical research collaboratively. You should be

Scale Labs, Research Scientist — Safety Post Training As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities. Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision. As a Research Scientist working on Safety Post-Training you will develop and apply post-training methods and interpretability techniques to make frontier AI systems safer, and better understood by researchers and policymakers.. For example, you might: Design and run post-training pipelines to study how training choices affect model safety, robustness, and alignment properties; Develop interpretability-informed evaluations that reveal how and why models produce unsafe, deceptive, or otherwise undesirable behaviors, and use those insights to guide targeted mitigations; Collaborate with policymakers, engineers, and other researchers to translate post-training and interpretability findings into actionable safety standards, evaluation benchmarks, and best practices. Ideally you’d have: Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance. Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches. A track record of published research in machine learning, particularly in generati

Scale Labs, Research Scientist — Frontier Risk Evaluations As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities. Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision. As a Research Scientist focused on Frontier Risk Evaluations, you will design and create evaluation measures, harnesses and datasets for measuring the risks posed by frontier AI systems. For example, you might do any or all of the following: Design and build harnesses to test AI models and systems (including agents) for dangerous capabilities such as security vulnerability exploitation, CBRN uplift, and other high-risk activities; Work with government agencies or other labs to collectively scope and design evaluations to measure and mitigate risks posed by advanced AI systems; Publish evaluation methodologies and write technical reports for policymakers. Ideally you’d have: Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance. Practical experience conducting technical research collaboratively. You should be comfortable building and instrumenting ML pipelines, writing evaluation harnesses, and quickly turning new ideas from the research literature into working prototypes. A track record of published research in m

MeltPlan | Planning Engine for the Built Environment MeltPlan is building the “planning engine” for the $14 Tn construction industry, an AI system designed specifically to optimize decisions before construction begins. While design software optimizes use and aesthetics and construction software optimizes execution and control, MeltPlan is building the missing layer - software that optimizes decisions and tradeoffs upstream, before scope is locked, procurement begins, and change orders become inevitable. MeltPlan’s long-term goal is to help teams make construction “boring” by making planning more intense: surfacing constraints and tradeoffs early, aligning stakeholders before plans are frozen, and reducing the need for late-stage redlines, rework, and change orders. MeltPlan is founded by operators who have built at scale. Kanav previously co-founded Innovaccer, a $3Bn healthtech company focused on making US healthcare more affordable and accessible. He’s now applying that systems-level thinking to construction.He’s joined by Tanmaya Kala, former Project Executive at DPR Construction, who led large commercial, healthcare, and life sciences projects. We combine deep tech scale with real construction execution. What This Role Really Is We are looking for an AI Research Scientist – Computer Vision to enhance and manage the PlanGraph model, which transforms 2D drawings into structured graphical representations of building elements. The role involves solving downstream business use cases such as quantity takeoff, code compliance, value engineering, and constructability analysis.We are specifically looking for hands-on researchers with experience in solving real-world Computer Vision problems and building custom vision models, VLMs, or VLLMs for production-grade applications. What You’ll Do Build and optimize custom Computer Vision models, VLMs, and VLLMs for construction intelligence workflows. Solve downstream business use cases including quantity takeoff, code complianc

About the Role OpenAI’s Industrial Compute organization is responsible for ensuring our compute infrastructure scales efficiently to support millions of users and increasingly sophisticated AI models. We’re looking for a Data Scientist to partner closely with Capacity Systems Engineering, Infrastructure, Product, and Research to optimize inference capacity across our global GPU fleet. This role combines statistical modeling, large-scale data analysis, forecasting, and systems thinking to drive critical decisions around infrastructure investments, performance-efficiency trade-offs, and customer experience. You’ll transform complex operational data into actionable insights that directly influence how OpenAI allocates and scales one of the world’s largest AI compute environments. Key Responsibilities Build statistical and machine learning models to profile and improve GPU utilization, latency, throughput, and overall fleet efficiency. Develop forecasting models for inference demand across products, regions, and model families. Analyze production workloads to identify latency bottlenecks and capacity constraints, highlighting optimization opportunities. Partner with Capacity Systems Engineering to inform infrastructure planning and long-term GPU investment strategies. Design experiments and simulations to evaluate scheduling policies, serving strategies, and infrastructure tradeoffs. Build dashboards and operational metrics that enable leadership to make data-driven capacity decisions. Collaborate with Product, Research, Finance, and Infrastructure teams to align compute planning with business growth and model roadmaps. Communicate technical findings clearly to both engineering teams and executive leadership. Qualifications MS or PhD in Statistics, Computer Science, Operations Research, Applied Mathematics, Economics, or related quantitative discipline (or equivalent industry experience). 5+ years of experience working in the infrastructure data science space. Strong ex

About the Team OpenAI’s People team hires, engages, and retains world-class talent to safely build and deploy AGI that benefits all of humanity. The People Analytics team helps leaders make better, evidence-based talent decisions. About the Role As a People Research Scientist, you will bring deep expertise in research design, measurement, experimentation, and applied data science to OpenAI’s most important People programs. You will design studies, evaluate people processes, and help leaders better empower employees, strengthen organizational systems, and deliver exceptional employee experiences. This is a high-ownership individual contributor role combining hands-on research, methodological leadership, and scalable people science capabilities. We’re looking for an experienced researcher who can turn ambiguous People questions into rigorous designs, validated insights, and actionable recommendations. This role is based in San Francisco, CA or Mountain View, CA, with occasional travel to our San Francisco office. What You’ll Do: Design rigorous research and evaluation strategies for recruiting, organizational health, manager effectiveness, employee experience, and talent outcomes. Apply advanced statistical modeling, machine learning, and research methods to inform program design, evaluate effectiveness, and quantify business impact. Partner with People Operations, data engineering, and people systems teams to define data requirements, improve data quality, establish documentation standards, and ensure research datasets are governed, reproducible, and privacy-preserving. Build scalable people science infrastructure, including self-service agentic tools, automated validation workflows, reusable research datasets and analytical pipelines. Develop research playbooks that establish rigorous standards for study design, measurement, validation, and documentation, enabling high-quality, repeatable, and scalable research across the organization. Communicate findings through c

About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. About the Role We’re looking for someone to help lead the future of analytics at Ramp. This person will enable Ramp to get 1% better every day by developing data products and insights. They will partner closely with business stakeholders and product, engineering, and design counterparts to prioritize and execute on work, improve reporting, as well as drive results and process improvements. What You’ll Do Full stack development, building models to consume, transform, and expose data to stakeholders and production systems Drive a culture of experimental design, testing agenda, and best practices Contribute to the culture of Ramp’s data team by influencing processes, tools, and systems that will allow us to make better decisions in a scalable way Collaborate with P/E/D/D (product, engineering, data and design) teams to develop product roadmaps and measure success Work closely with data engineering teams to capture, move, store, and transform raw data into highly actionable insights, and partner with business teams to turn those insights into action Wha

About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. What You’ll Do Full stack development, building models to consume, transform, and expose data to stakeholders and production systems Drive a culture of experimental design, testing agenda, and best practices Contribute to the culture of Ramp’s data team by influencing processes, tools, and systems that will allow us to make better decisions in a scalable way Collaborate with Finance teams (e.g. GTM Finance, StratFin) to develop financial insights and influence business decisions Work closely with data engineering teams to capture, move, store, and transform raw data into highly actionable insights, and partner with business teams to turn those insights into action What You Need Minimum of 3 years of industry experience in Data Science / Software Engineering / Finance Strong AI proficiency as a lever to quickly adopt new skills and subject matter Track record of shipping high quality products and features at scale Ability to thrive in a fast-paced, constantly improving, start-up environment that focuses on solving problems with iterative technical so

Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. About the Role: We're redefining how software is built and who gets to build it. Our mission is to achieve Autonomy for All: making programming accessible, collaborative, and powered by AI. To realize this vision, we need people who are obsessed with understanding how users experience our product and can turn that understanding into decisions that move the needle. You'll directly impact Replit's growth by turning user behavior into actionable insights that shape product strategy, improve activation and retention, and drive sustainable revenue growth across our self-serve and enterprise segments. Who You are You're a data scientist who moves fast and goes deep. You can spin up an analysis in hours that would take others days; not by cutting corners, but because you've built the intuition and technical toolkit to get to the right answer quickly. You're the person who digs past the top-line number to find the confound, questions whether the metric actually measures what people think it does, and pressure-tests your own work before anyone else sees it. You treat experimentation as a craft, not a checkbox. You've felt the pain of underpowered tests and novelty effects, and you have the judgment to get it right. You use AI agents and tools aggressively to multiply your output - writing code, exploring data, generating hypotheses, but you treat every AI-assisted output as a draft, not a deliverable. You know what good analysis looks like and you won't ship anything that doesn't meet that bar. The result is that you operate at a speed and depth that most DS teams can't match. You will: Design and analyze product experiments to evaluate feature launches, onboarding changes, and in-product interventions with rigorous statistical

Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. About the Role We're redefining how software is built and who gets to build it. Our mission is to achieve Autonomy for All: making programming accessible, collaborative, and powered by AI. Realizing that vision requires a platform that legitimate users can trust and adversarial actors cannot exploit. We're hiring a Data Scientist to help build Replit's Trust & Safety and Anti-Abuse program from the ground up. You'll turn noisy behavioral, identity, payment, infrastructure, and content signals into the measurement systems, detections, and decisions that protect Replit's users, platform, and economics. You'll work closely with Engineering, Support, Legal, Security, Infrastructure, Money, and Growth to make abuse economically unviable while keeping friction low for legitimate users. Replit sits at the frontier of AI-native abuse. Our platform is a target for phishing and scam hosting, cryptomining, LLM token farming, card and coupon fraud, referral abuse, and increasingly, abuse driven by AI agents themselves. You'll help define how we identify, measure, and respond to these threats without compromising the experience of good users. Who You Are You're a data scientist who moves fast, goes deep, and thinks adversarially. You can spin up an analysis in hours that would take others days, not by cutting corners, but because you've built the intuition and technical toolkit to get to the right answer quickly. You dig past the top-line abuse rate to understand selection effects, missing labels, policy changes, attacker adaptation, and the false positives hidden inside an aggregate metric. You understand that Trust & Safety data is imperfect and outcomes are high stakes. Ground truth is delayed, biased, and often incomple