Top Data Science Jobs

Data Science interns apply big data and advanced analytics to identify data sources, process datasets, develop algorithms, perform exploratory analysis, engineer features, build models, and generate business recommendations. They collaborate with data and software engineering teams to productionize algorithms and may receive opportunities for technical coaching and expertise development.

Develop and deploy NLP, machine learning, and deep learning solutions for intelligence and national security applications. Build text classification, topic modeling, information extraction, and language-model systems using Python, PyTorch, TensorFlow, Keras, and Hugging Face. Analyze large personnel, intelligence, and performance datasets with SQL and advanced statistics, using GPU computing to accelerate model development. The role is on-site and requires an active TS/SCI clearance with polygraph and at least 10 years of experience.

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.

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.

Analyze large structured and unstructured datasets, develop and deploy statistical and machine learning models, build/automate data processing workflows, create visualizations and dashboards, and communicate findings to technical and non-technical stakeholders in support of IC and DoD missions.

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

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.

The Data Scientist will analyze complex data sets, manage data consistency, create visualizations, build mathematical models, and communicate findings effectively.

The Data Scientist will analyze large datasets using statistical software to identify insights and present findings to stakeholders, manage projects, and apply machine learning techniques.

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 develop and analyze complex queries on large datasets, manipulate structured and unstructured data, and provide analytic support using various tools and techniques in clustered computing environments.

The Data Scientist supports AI-driven initiatives, conducts analytics, builds machine learning algorithms, and develops technical reports. They work with large datasets to drive business improvements and enhance patient lifetime value.

Lead the data science strategy for deployed humanoid robots: build predictive maintenance and reliability models, drive fleet performance and RaaS economics analysis, develop anomaly detection and RCA tooling, link manufacturing test data to field outcomes, and set instrumentation, experimentation, and data-quality standards across hardware, software, manufacturing, and operations.