Top Hybrid Data Science Jobs

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.

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.

Analyze large datasets using quantitative methods, data mining, statistical modeling, and AI tools to generate actionable product and business insights. The role designs and tracks product metrics, builds dashboards and reports, identifies trends and opportunities, and supports data-driven decisions. Responsibilities include collaborating with product managers, engineers, marketers, and designers, applying statistical techniques such as A/B testing and causal analysis, and independently driving projects to completion.

Develops data workflows, statistical models, and machine learning solutions using tire, vehicle, simulation, telemetry, testing, and race data. Analyzes tire performance, operating windows, degradation, and strategy impacts while delivering dashboards, reports, notebooks, and decision-support tools. Partners with engineering, data, and motorsports teams, leads complex analytics projects, establishes reproducible modeling practices, and mentors engineers and analysts.

Analyzes complex data sets, develops Python-based data solutions and pipelines, and applies advanced analytics to support client strategy and business performance. Designs dashboards and visualizations, collaborates with cross-functional teams, manages stakeholders, improves data quality, and builds client relationships. The role also involves solving complex business challenges, identifying industry trends, and delivering high-quality work within PwC’s Risk & Regulatory practice.

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.

Manage AI and data science projects from planning through implementation, coordinating technical and non-technical teams, dependencies, Agile ceremonies, requirements, budgets, timelines, scope, and stakeholder expectations. Translate AI capabilities into actionable plans, navigate ambiguity, and drive accountability across multiple teams and priorities.

Research intern supporting advanced analytics and new product development for revenue management. Responsibilities include preparing and analyzing data, identifying problems, designing and implementing solutions, researching forecasting, pricing, optimization, generative AI, and machine learning applications, presenting findings, and producing technical documentation. The role involves Python, SQL, Pandas, LLMs, RAG, and prototyping with tools such as LangChain and visualization frameworks.

Leads advanced data science projects involving machine learning, deep learning, statistical modeling, forecasting, segmentation, search, recommendations, ranking, and generative AI. Develops production-grade code and evaluation frameworks, deploys scalable models, collaborates with business and technical teams, communicates insights, and mentors junior data scientists.

Develops advanced analytics and machine learning models for client business challenges, particularly using pharmaceutical and healthcare data. Translates complex analyses into actionable business recommendations, communicates findings to clients, manages analytical workstreams, supports project milestones, builds client relationships, and mentors junior team members.