Top Senior Data Science Jobs

Develops scalable data pipelines, parsers, and database systems for enterprise AI applications. Collaborates with AI/ML teams to optimize data access, retrieval, quality, security, and infrastructure performance. Supports AI application deployment and maintenance while evaluating machine learning models and statistical techniques. The role requires expertise in Python, databases, Docker, Kubernetes, data engineering, defensive cyber operations, and machine learning, with potential supervision of others.

Develop and optimize scalable data pipelines, parsers, and database systems supporting enterprise AI applications. Ensure data quality, security, and efficient retrieval across graph, SQL, NoSQL, and vector databases. Collaborate with AI/ML teams, support AI application deployment, and optimize infrastructure for performance, cost, and scalability. The role requires advanced data engineering expertise, cloud familiarity, and an active TS/SCI clearance with polygraph.

Designs, deploys, and maintains AI, machine learning, and generative AI solutions supporting sales forecasting, pipeline analytics, lead scoring, customer segmentation, and seller productivity. Builds predictive models, AI copilots, assistants, and RAG solutions; analyzes CRM and operational data; develops dashboards and executive reporting; and establishes MLOps monitoring. Partners with sales, revenue operations, and IT leaders to identify use cases, deliver scalable solutions, and drive data-informed commercial decisions.

As a Senior Data Scientist, you will develop technologies for signals exploitation, perform data analysis, and implement algorithms while collaborating with project teams.

Design, build, validate, deploy, and monitor ML models (credit risk, fraud, transaction timing). Create training datasets, run A/B tests, analyze customer behavior, and collaborate with Engineering, Product, and Analytics to deliver end-to-end data science solutions.

Lead Data Scientist will define goals and metrics for algorithm Test & Evaluation, research T&E methodology, lead vendor coordination, present analyses and results to stakeholders, and transition algorithms into software and real-time embedded systems.

Leads graph analytics, identity resolution, forecasting, and predictive modeling initiatives for operational, commercial, and product decisions. Designs scalable graph data models, production-ready machine learning solutions, and time-series forecasts. Establishes validation, monitoring, governance, and experimentation practices while partnering with product, engineering, and platform teams. Translates complex analytical findings into business recommendations and provides technical leadership across graph science, forecasting, optimization, and decision-support capabilities.

The Senior Data Scientist will analyze large-scale customer data, investigate behavioral questions, design and automate studies, and translate findings into actionable recommendations. The role includes maintaining analytics frameworks, documenting methodologies, writing white papers, and communicating insights across the company. Candidates should be independent, comfortable with ambiguity, and experienced with SQL and Redshift. Python or R, machine learning, Spark, A/B testing, and cross-functional experience are preferred.

Lead SMB Ads analytics end-to-end: define growth KPIs, diagnose funnel friction, build propensity/LTV models, design attribution and A/B experiments, automate dashboards, and forecast advertiser/revenue growth to inform product and marketing decisions.

Lead measurement and insights for ads demand: define metrics and benchmarks, analyze advertiser performance, build early-warning signals, design experiments, translate findings into sales and product actions, forecast opportunities, and close feedback loops to improve advertiser outcomes.

Lead the design, development, and deployment of AI-powered self-service analytics products and production data systems. Build LLM-augmented workflows, evaluate model quality and reliability, establish monitoring, and automate insights. Partner with AI engineers, software engineers, data scientists, and business stakeholders to deliver scalable tools, shape the AI analytics roadmap, and independently manage projects from conception through launch.

Develop and validate AI-driven legal product capabilities using NLP, LLMs, generative AI, RAG, hybrid search, and agentic workflows. Analyze large-scale legal datasets, design model evaluation and benchmarking practices, translate experiments into product recommendations, collaborate with product, legal, and engineering teams, and mentor data scientists while providing technical leadership.

Develops statistical and machine learning solutions by processing, analyzing, and visualizing complex datasets. Builds data models, optimizes queries, creates dashboards, and communicates insights supporting business and mission objectives. Requires software development, containerization, Kubernetes deployment, Git workflows, Linux, Agile practices, configuration management, and release support. Preferred experience includes production microservices, REST APIs, event-driven architectures, PostgreSQL optimization, Redis, and cloud-native scalability, availability, and fault tolerance.

Develop and deploy machine learning models powering real-time bidding, advertising decisions, reporting, and customer predictions. Solve propensity modeling, bidding strategy, A/B testing, CLTV, and churn prediction problems at large scale. Build enterprise-grade data and machine learning platforms, write modular production code, collaborate with engineers, and own projects from concept through implementation.

Build and deploy production-grade LLM applications, AI agents, models, and scalable RAG pipelines for investment and business workflows. Architect secure MCP integrations with enterprise systems, establish MLOps and LLMOps practices, optimize model performance and latency, and develop evaluation, monitoring, governance, and rollback processes. Collaborate with data engineering and platform teams to provide secure, governed access to organizational data.

As a Data Scientist, you will advance applied ML capabilities for healthcare, develop LLM-based chart abstraction techniques, and enhance data science best practices.

As the first Data Scientist, you will analyze user data and AI outputs to enhance content creation, define metrics, conduct A/B testing, and collaborate closely with cross-functional teams to improve product features.

The Senior Data Scientist will lead the modernization of Impact's identity graph infrastructure by auditing and improving its construction, quality, and resolution methods, while collaborating with external partners and maintaining documentation and research.

Lead data science across autonomy, safety, and product by analyzing machine telemetry and operational data, defining metrics, designing experiments, and partnering with ML platform and engineering to deliver insights that improve system performance and safety.

The Data Scientist Senior Consultant will lead data analytics projects, manage client relationships, and develop data solutions using various tools. They require a strong background in data science and communication skills.

The Data Scientist will enhance OpenAI's experimentation platform, improve statistical rigor, develop analytical systems in Python, and mentor other data scientists.

Own complex data science projects applying machine learning, generative AI, statistical methods, and natural language processing to legal knowledge management and regulatory intelligence. Build systems for retrieval, summarization, classification, requirements extraction, impact analysis, and decision support. Establish data foundations, evaluation metrics, human-review workflows, and responsible AI practices while partnering with legal, policy, research, engineering, and business stakeholders. Mentor practitioners and operationalize scalable, auditable solutions.

Develop and maintain advanced data processing, entity resolution, and data cleansing pipelines using Python and SQL. Responsibilities include NLP, information extraction, data validation, query optimization, testing, deployment, monitoring, code reviews, and maintaining data integrity, privacy, security, and reproducibility. The role requires expertise in record linkage, named entity recognition, text processing, matching strategies, and machine learning libraries, with on-site work at SSA headquarters in Woodlawn, Maryland.

Lead evaluation and improvement of the U.S. Census Bureau’s LLM Autocoder for survey write-in responses. Establish performance baselines, design reproducible experiments, analyze coding accuracy and errors, compare LLM and rule-based methods, assess expansion to additional survey fields, and guide engineers on model and preprocessing changes. Collaborate with government subject matter experts, interpret pilot and production results, and document recommendations and reusable technical practices.

Senior Data Scientist partnering with Product, Design, Engineering, Finance, and Marketing to drive user growth through data science, causal inference, experimentation, and analytics. Responsibilities include developing LTV and retention models, designing A/B tests, creating measurement frameworks and dashboards, translating complex analyses into recommendations, and strengthening data foundations for scalable experimentation.