Top Data Science Jobs

Leads alternative data strategy for credit decisioning by evaluating vendors and datasets, engineering features, building and validating machine learning models, and developing scalable processing and monitoring systems. Partners with data scientists, software engineers, and product managers while translating complex analyses into business outcomes. The role requires expertise in large-scale data analysis, statistical modeling, machine learning, and relational databases, with hybrid office attendance.

Develop machine learning models and scalable data-processing systems using alternative datasets for credit decisioning. Evaluate vendors and data products, engineer features, validate and monitor models, build challenger models, and guide dataset applications. Collaborate with data scientists, software engineers, and product managers while translating complex statistical insights into business outcomes. The role uses Python, AWS, Spark, H2O, and other open-source technologies across large numeric and textual datasets.

Lead the development of deep learning and computer vision models for MRI, X-ray, CT, and PET medical imaging in clinical trials. Build image processing pipelines, analyze disease progression and treatment response, develop imaging biomarkers, integrate AI into trial workflows, support FDA-compliant endpoints, and present findings to clinical, regulatory, executive, and external stakeholders.

Lead data science initiatives supporting drug development, including patient stratification, biomarker identification, therapeutic hypothesis evaluation, clinical trial optimization, and AI agent development. The role applies machine learning, computational biology, and advanced analytics to biomedical data while collaborating with clinical, research, and engineering teams. Responsibilities also include presenting complex findings to senior stakeholders and contributing to production-oriented AI solutions.

Embedded Data Scientist for Superhuman Mail, partnering with product, growth, engineering, and design teams to guide roadmap decisions. Responsibilities include defining product metrics, designing experiments, applying causal inference and machine learning, measuring AI feature quality, analyzing activation and retention, identifying growth and conversion drivers, and communicating insights to stakeholders. The role requires strong Python, SQL, experimentation, statistics, and cross-functional influence skills.

Develops machine learning models and scalable data processing systems for credit decisioning using alternative datasets. Evaluates vendors, engineers features, validates and monitors models, builds challenger models, and provides data domain expertise. Collaborates with data scientists, software engineers, product managers, and business stakeholders to translate complex analyses into business outcomes. The role requires statistical modeling, big data analysis, cloud computing, and cross-functional communication.

Develops anti-money laundering analytics and machine learning models using customer, account, and transaction data. Responsibilities include data preparation, statistical analysis, model building, monitoring, documentation, experimentation, and communicating results to executives and stakeholders. The role applies methods such as logistic regression, clustering, gradient boosting, and neural networks to identify suspicious activity and improve AML strategies. It also involves model validation, production implementation, cross-functional collaboration, and management of multiple projects.

Leads the design, development, deployment, and monitoring of AI and machine learning models for supply chain decision-making. Extracts and integrates complex data, performs analysis, develops forecasting and optimization solutions, and embeds models into business workflows. Communicates findings to technical and non-technical stakeholders while partnering with business and technology teams to advance AI adoption and deliver measurable impact.

Leads data science for the American Dream Initiative by developing customer outcome metrics, financial health indicators, personas, automated reporting, OKR frameworks, and experimentation programs. Integrates large-scale data, performs analyses and modeling using SQL and Python, and delivers executive-ready insights for targeting, personalization, and customer engagement. Partners cross-functionally, mentors team members, and ensures responsible analytics, compliance, and model-risk practices.

Conduct statistical analysis, evaluate product experiments, analyze user interaction data, identify growth opportunities, and build dashboards, notebooks, or AI agents. Partner with data science, engineering, and business teams to communicate actionable insights. The internship requires full-time participation for 12 weeks in the Seattle hub and enrollment in a master's program with graduation by August 2028.

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Leads people analytics and quantitative insights initiatives using statistical analysis, machine learning, data modeling, and visualization. Analyzes complex workforce datasets, develops predictive algorithms and dashboards, identifies optimization opportunities, and communicates actionable recommendations to technical and business stakeholders. The role manages multiple analytical projects and partners with data architects, engineers, analysts, and visualization experts. It requires a master’s degree and typically five or more years of relevant experience.

Early-career data scientist supporting sector investment teams: deliver data pulls, analyses, trackers, dashboards, anomaly investigations, and reusable Python/SQL tooling. Use time-series methods, alternative data, and LLMs to accelerate workflows and build commercial judgment with investors.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Build production-grade marketing mix, forecasting, customer lifetime value, and incrementality models to guide budget allocation. Develop Databricks and Delta Lake ingestion pipelines, integrate GenAI agents for anomaly detection and reporting automation, and translate statistical insights into Tableau dashboards and executive strategy. The role also supports stakeholder training in Databricks SQL and requires at least one in-person interview.

Develops and deploys interpretable machine learning, data science, and Generative AI solutions for Corporate and Institutional Banking. Responsibilities include analyzing large datasets with Python or R, building dashboards, developing and monitoring models, defining performance metrics, and transitioning analytical prototypes into production. The role collaborates with business, product, engineering, and testing teams to translate business needs into scalable data products across sales, credit, underwriting, and operations.

Build and deploy predictive models, recommendation systems, LLM applications, incentive optimization frameworks, and causal inference models for customer growth. Partner cross-functionally to improve acquisition, engagement, retention, and expansion. Measure marketing impact, guide investment decisions, develop AI analytics and decision systems, and provide technical leadership and mentorship across data science initiatives.

Own product analytics and data science for a business-critical product area. Develop production-ready churn, resurrection, and targeting models; define metrics; analyze experiments; apply causal inference; and deliver actionable recommendations with quantified business opportunities. Partner closely with Product, Engineering, Design, and Growth teams while contributing to experimentation methodology, standards, and tooling.

Lead machine learning and data science initiatives supporting workforce planning, including demand forecasting, capacity planning, resource scheduling, and business analysis. Design, develop, validate, and document statistical, AI, and machine learning models; lead complex projects; mentor junior team members; partner with stakeholders; and communicate data-driven recommendations. The role requires advanced quantitative expertise, model governance experience, and proficiency with data science programming and big-data technologies.

Develops advanced analytics and machine learning models using structured and unstructured data to generate actionable business insights. Responsibilities include defining analytics requirements, selecting modeling methodologies, integrating models into business processes, measuring performance, enhancing analytics solutions, creating visualizations, and communicating findings to stakeholders. The role requires collaboration with application developers and independent judgment in solving technical and business problems.

Leverage website and app behavioral data to drive user acquisition, engagement, retention, conversion, and growth. Design experiments, apply causal inference, build predictive and forecasting models, segment users, measure business impact, and communicate recommendations to Product, Engineering, and business leaders.

Analyze website and app behavioral data to drive user acquisition, engagement, retention, conversion, and growth. Responsibilities include segmentation, experimentation, causal inference, predictive modeling, forecasting, impact measurement, and collaboration with Product and Engineering leaders to shape strategy and communicate actionable recommendations.

Staff Data Scientist partnering with Growth, Marketing, Product, and Engineering teams to build data science solutions that drive customer acquisition, engagement, and retention. Responsibilities include predictive modeling, personalization, recommendation systems, LLM applications, incentive optimization, causal inference, marketing impact measurement, technical mentorship, and strategic leadership. The role requires deploying production machine learning systems and communicating analytical insights to senior stakeholders.

Lead payments-focused data science work: build foundational datasets, metrics, and scalable analytics; analyze customer and transaction behavior (including onchain data); conduct strategic analyses to identify product and revenue opportunities; build reporting, dashboards, and automation; and influence product and business strategy through data storytelling to senior stakeholders.

Lead the development and deployment of predictive models, optimization algorithms, AI agents, and decision-support tools for commercial and pricing functions. Partner with business leaders to translate challenges into scalable analytics solutions, improve forecasting, pricing, sales performance, and commercial strategy, and deliver measurable business outcomes. Provide technical mentorship, promote analytics best practices, and help operationalize generative AI, LLM, and RAG solutions.