Top Data Science Jobs

Analyze large datasets using data architecture, Java, sentiment analysis, OpenNLP, data mining, and machine learning techniques. The role requires developing or supporting big data analytics solutions and applying machine learning methods to derive insights from complex data.

Lead technical direction for homeowners insurance pricing and risk modeling. Architect and deploy GLM and machine learning models, including profitability, retention, conversion, and customer lifetime value models. Use geospatial data to assess localized risk, partner with actuarial, product, and engineering teams on regulatory filings and production workflows, mentor data scientists, and champion experimentation, monitoring, MLOps, and emerging generative AI applications.

Develop predictive models and data science solutions for automated credit-risk and lending decisions. Responsibilities include collecting, cleaning, visualizing, and analyzing large datasets; identifying patterns; presenting insights to stakeholders; translating models into business strategies; partnering with IT on implementation; and monitoring model performance.

Designs and delivers onboarding, upskilling, and in-person training for data scientists; creates training materials and playbooks; establishes Git, code review, and continuous integration best practices; documents tools and methodologies with Sphinx; and partners with stakeholders to improve tooling and priorities. The role also requires advanced Python and SQL expertise, Linux and Jupyter proficiency, debugging ability, coaching skills, and collaboration across stakeholders.

Develop statistical, physical, and atmospheric models for detecting, attributing, localizing, and quantifying trace gases and emissions. Analyze time-series, geospatial, sensor, and scientific datasets; design experiments; quantify uncertainty; and translate research models into production cloud-platform components. Collaborate with analytics, product, engineering, and customer success teams while documenting and communicating findings to technical and non-technical stakeholders.

Works with business stakeholders to identify data-driven opportunities, analyze trends, and support business decisions. Designs data models, machine-learning algorithms, and predictive analytics models; preprocesses structured and unstructured data; identifies data sources and automates collection; analyzes large datasets for patterns; and presents insights through data visualization.

Leads data science work involving data exploration, cleaning, analysis, visualization, mining, predictive modeling, and algorithm development. Analyzes structured and unstructured data using statistical and programming tools, applies machine learning and AI techniques, and translates complex datasets into actionable insights for clients. Guides teammates, develops data-driven systems, and advises decision-makers across public and private sector challenges.

Lead the research, design, implementation, and deployment of AI models, agentic solutions, and optimization algorithms for enterprise applications. Partner with customers and cross-functional teams to build scalable AI applications, improve model performance, and integrate validated data assets. Contribute to new platform capabilities while applying generative AI, large language models, classical machine learning, and statistical methods in reliable, production-scale environments.

Analyze structured and unstructured business and mission data using quantitative and qualitative methods. Clean, transform, validate, and combine datasets with Python, SQL, Excel, Tableau, and data preparation tools. Build dashboards and visualizations, identify trends and relationships, translate customer requirements into analytical specifications, document workflows, and present actionable findings to technical and non-technical stakeholders. The role requires independent task management, collaboration with customers, and fully onsite work with a recent polygraph clearance.

Analyzes large, complex datasets to support mission needs by integrating multi-source data, applying statistical and modeling techniques, building rapid analytic prototypes, and creating reproducible Python workflows with Jupyter and Pandas. Evaluates data quality, develops performance metrics, translates operational questions into analytic requirements, and communicates findings to technical and non-technical stakeholders. The role collaborates with multidisciplinary teams and requires a current government clearance, background investigation, and polygraph.

Designs, prototypes, evaluates, and improves LLM-powered legal AI systems and agentic workflows. Responsibilities include prompt engineering, RAG, retrieval and ranking, model experimentation, evaluation benchmarks, error analysis, monitoring, and production-oriented coding. The role engages lawyers and enterprise stakeholders to define measurable problems, validate AI experiences, and establish quality standards for accuracy, grounding, hallucination detection, tool use, reliability, cost, and latency.

Evaluates and improves statistical models, machine learning algorithms, and analytic workflows supporting classified intelligence and Space and Counterspace missions. Analyzes complex datasets, validates algorithm performance, assesses data quality, integrates new analytic approaches, and provides technical recommendations and briefings to government stakeholders. Collaborates with engineers, developers, analysts, and data science teams while ensuring security and data governance compliance.

Build and own marketing measurement, attribution, experimentation, and analytics for performance, influencer, and lifecycle marketing. Responsibilities include creating dashboards, debugging attribution across advertising platforms and CRM systems, designing incrementality and A/B tests, analyzing CAC, ROAS, payback, cohorts, LTV, and channel efficiency, developing attribution taxonomies and instrumentation, and translating findings into recommendations for marketing, product, finance, and executive stakeholders.

Develops and manages AI and machine learning product roadmaps, leads cross-functional teams, oversees model development and deployment, and monitors model performance. Responsibilities include data preparation, exploratory analysis, LLM and NLP integration, governance, bias mitigation, compliance, risk management, stakeholder communication, product optimization, Agile delivery, and mentoring junior staff.

Analyze complex datasets to identify trends, build predictive models, and apply machine learning to support business decisions. Collaborate with cross-functional teams, communicate insights to technical and non-technical stakeholders, ensure security and confidentiality compliance, and mentor junior data scientists and analysts. The role requires working with statistical methods, data visualization, SQL, machine learning frameworks, and classified government data in a fully onsite, defense-related environment.

Develop and apply advanced AI and machine learning methods to aging biology research. Build computational approaches for large-scale biological and imaging data, including computer vision, segmentation, deep learning, generative AI, diffusion, multimodal, and foundation models. Analyze complex datasets, identify biologically meaningful patterns, collaborate with experimental and computational researchers, and help shape research at the intersection of AI, imaging, and aging.

Develop AI-enabled systems for harmonizing, modeling, and interpreting large biomedical and multi-omics datasets. Build LLM-powered tools, agentic workflows, RAG systems, data pipelines, and predictive models supporting computational biology and aging research. Collaborate with scientists and engineers on data curation, literature mining, annotation, reproducibility, documentation, manuscripts, and research software. Apply machine learning, network analysis, clustering, classification, and biological interpretation to transcriptomics, proteomics, metabolomics, clinical, imaging, and phenotypic data.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Designs and delivers machine-learning, analytics, NLP, and generative-AI solutions for healthcare and regulated federal data environments. Builds production workflows, predictive models, classification systems, and decision-support tools using structured and unstructured data. Owns data quality, experimentation, evaluation, validation, monitoring, governance, privacy, and deployment decisions. Collaborates with technical teams, domain experts, clients, and leadership to solve ambiguous, technically complex problems.

Lead growth data science for emerging healthcare products by defining measurement frameworks, analyzing funnels, designing experiments, building predictive models and segmentations, and creating reliable data foundations, dashboards, and self-service tools. Partner with Marketing, Product, Engineering, Design, and Data Science to optimize acquisition, engagement, conversion, launches, and product growth.

Leads product analytics through advanced analysis, experimentation, KPI development, dashboards, and data storytelling. Designs A/B tests and causal analyses to optimize product performance and user experience. Owns measurement for Xometry’s embedded DFM AI and IQE integration across CAD/PLM partner surfaces and the marketplace, addressing attribution, identity resolution, and low-volume enterprise cohort challenges. Collaborates with product, engineering, design, machine learning, and partner teams to deliver actionable insights and improve experimentation methodologies.

Lead product analytics for Xometry’s marketplace and partner-integrated CAD/PLM experiences. Design and analyze A/B tests, causal studies, KPI frameworks, dashboards, and cross-surface measurement strategies. Evaluate user behavior, product performance, and digital-thread outcomes across partner and marketplace funnels. Translate statistical findings into product recommendations, collaborate with product and engineering teams, ensure data integrity, and communicate insights to leadership and external partners.

Analyze product and user data to guide product strategy, operations, and roadmaps. Design experiments, apply causal inference, investigate anomalies, define metrics, build statistical models and simulations, and establish scalable analytics practices. Present actionable insights to technical and business stakeholders while supporting data-informed decision-making for AI products.

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.