Top Data Science Jobs

Develops statistical and machine-learning models for pricing strategy, revenue optimization, demand forecasting, segmentation, and utilization analysis. Designs experiments, validates and documents models, prepares large-scale datasets, and partners with data engineering teams on scalable pipelines and deployment. Translates analytical outputs into pricing recommendations, dashboards, and decision tools while supporting governance, stakeholder presentations, and analyst enablement.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Develop AI-enabled applications, intelligent workflows, agent-based solutions, and enterprise automation. Build scalable data pipelines, APIs, integrations, analytics-ready datasets, and structured data models. Apply LLMs, embeddings, vector databases, semantic search, and orchestration frameworks to prototype and deploy practical AI solutions. Collaborate with technical and business stakeholders to identify transformation opportunities, support experimentation, and ensure scalable adoption across commercial, operational, product, marketing, and technology functions.

Supports the design, development, deployment, testing, and monitoring of AI and machine learning solutions for business problems. Builds agentic AI prototypes, translates functional requirements into ML solutions, evaluates frameworks and algorithms, documents development standards, analyzes complex datasets, and communicates insights to technical and non-technical stakeholders. Projects may include demand forecasting, manufacturing optimization, ingredient formulation, yield management, and automated decision-making.

Uses statistical modeling, machine learning, simulation, and visualization to solve business data problems. Responsibilities include identifying and ingesting data sources, evaluating data quality, building extraction pipelines, analyzing and visualizing data, developing predictive models, documenting reproducible analyses, gathering business requirements, managing priorities and dependencies, and presenting actionable insights to stakeholders. The role also supports developing data infrastructure that converts experiments into usable products.

Build and maintain predictive models throughout their lifecycle, identify data patterns and business insights, develop analytic procedures and automation, perform complex ad hoc analyses, monitor model recalibration, and review model scoring and data transformation quality controls.

Build and maintain predictive models throughout their lifecycle, identify data patterns and trends, deliver decision-making insights, and develop analytic procedures and automation. The role also owns model recalibration, performs ad hoc analyses, addresses client strategy needs, and reviews model scoring and data transformation quality controls.

Own the optimization and decision science capabilities for Crucible, Firestorm’s manufacturing planning software. Develop production, sourcing, inventory, scheduling, allocation, and network optimization models; address uncertainty; validate and deploy production-ready solutions; and collaborate with software, manufacturing, supply chain, and product teams. Set technical direction, establish modeling standards, and build the foundation for future data science efforts.

Develop predictive models, algorithms, quantitative analyses, and visualizations from structured and unstructured data. Collaborate with clients to understand business and mission needs, uncover insights, guide teammates, and translate complex datasets into actionable recommendations. The role uses programming, statistical analysis, machine learning, and distributed computing techniques across public and private-sector challenges.

Develops and deploys production machine learning models and data products for enterprise use. Responsibilities include data mining, feature engineering, model evaluation, NLP and LLM/RAG solutions, API and pipeline integration, cloud-based deployment, model validation, A/B testing, documentation, and lifecycle management. The role translates business needs into scalable data science solutions and collaborates with product, engineering, and business stakeholders throughout the SDLC.

Analyze structured and unstructured data to uncover insights, develop predictive models and algorithms, and create visualizations that support client decision-making. The role involves data exploration, cleaning, mining, statistical analysis, machine learning, artificial intelligence, and natural language processing across public- and private-sector challenges. Candidates collaborate with clients, use programming and distributed-computing tools, and must hold a Secret clearance and bachelor’s degree.

Build and deploy AI/ML solutions from prototype through production for CMS, including generative AI, LLMs, RAG, intelligent classification, knowledge management, agentic frameworks, and enterprise integrations. Evaluate AI coding tools, establish AI/ML roadmaps, scale adoption, mentor data scientists, and translate technical concepts for stakeholders and leadership.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

Supports Naval Surface Warfare Center mission engineering by applying data science, statistics, machine learning, data mining, graph algorithms, and operations research to electromagnetic spectrum data. Builds and maintains data pipelines and analytical systems, performs statistical investigations, supports software integration and testing, develops scripts and APIs, and converts large datasets into decision-quality insights.

Develops end-to-end AI and machine learning solutions for enterprise marketing, including predictive, Bayesian, NLP, deep learning, graph, and reinforcement learning models. Builds automated MLOps pipelines, model-serving APIs, dashboards, and containerized Kubernetes deployments. Oversees model evaluation, monitoring, drift mitigation, and continuous improvement while translating business needs into technical roadmaps. Communicates model performance and recommendations to technical, executive, and business stakeholders.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Develop predictive models, algorithms, quantitative analyses, and visualizations from structured and unstructured data. Analyze complex datasets, apply statistical and machine learning techniques, and collaborate with clients to turn data into actionable insights for public- and private-sector challenges.

Analyze complex national security missions and operational environments; develop strategic frameworks, visualizations, exercises, and wargaming approaches. Assess AI and ML capabilities, including risks, vulnerabilities, and adversarial factors. Integrate data feeds, create dashboards, conduct capability assessments, collaborate with industry and academia, and prepare executive recommendations and briefings for senior leaders.

Develop and deploy AI and machine learning solutions, analyze complex enterprise datasets, build predictive and optimization models, conduct statistical experiments, and translate findings into business recommendations. The role includes applying deep learning, NLP, generative AI, and ethical AI practices; creating scalable decision-support frameworks; evaluating emerging technologies; collaborating across teams; and mentoring junior data scientists and analysts.

Develop and deploy predictive, statistical, and machine learning models for enterprise business decisions, including forecasting, segmentation, propensity modeling, optimization, and fraud detection. Partner with cross-functional stakeholders, improve cloud analytics infrastructure, evaluate data sources, communicate insights, and mentor junior data scientists and analysts.

Lead end-to-end generative AI and machine learning initiatives, designing secure and scalable architectures, developing and deploying advanced models, analyzing healthcare data, conducting experiments, and establishing MLOps processes. Research emerging AI methods, promote responsible AI practices, and mentor or recruit AI and machine learning talent. Collaborate with software engineers to productionize models and deliver reusable capabilities for healthcare solutions.

Provides technical and analytical leadership for enterprise data science and AI initiatives. Designs and deploys AI/ML solutions, develops predictive and causal models, leads experimentation and validation, analyzes large-scale datasets, and translates findings into executive recommendations. Establishes responsible AI, governance, explainability, monitoring, and reproducibility standards while mentoring data scientists. Applies Generative AI, LLMs, agentic AI, and advanced statistical methods to create scalable decision-intelligence capabilities and measurable business value.

Design and execute product analyses using raw user behavior data, own A/B experiments from design through interpretation, and translate findings into recommendations for product and engineering teams. Build repeatable analytical tooling, apply statistical modeling, and use AI tools to accelerate exploration, analysis, and insight delivery across consumer products, media, gaming, and e-commerce.

Develops predictive modeling algorithms and processes large data streams using distributed computing environments. Leads complex data science projects, model scoring, algorithm development, visualization, and actionable business insights. Partners with stakeholders to identify population segments and patterns related to quality, cost, and utilization. Drives continuous model improvement and mentors other data scientists.

Lead end-to-end dynamic pricing models using demand modeling, causal inference, market simulation, and optimization. Develop pricing approaches for elasticity, seasonality, pacing, and lead-time dynamics; design experiments measuring marketplace effects; and translate complex business problems into deployed solutions. Own production Python code, model deployment, monitoring, retraining, reproducibility, and ML operations while influencing product strategy and collaborating with technical and executive stakeholders.