Role Description As a Senior Manager of Product Data Science, you will be a key leadership figure within the Product Data Science organisation, responsible for building and scaling a high-performing team of Analysts and Data Scientists embedded within Product domains. You will not only deliver impact through your team, but also raise the overall analytical and technical bar of the organisation, ensuring data science, experimentation, and product analytics are consistently applied at a high standard across multiple product areas. You will act as a force multiplier for decision-making quality, improving how Product, Engineering, Design, and Commercial teams use data to shape strategy, prioritise work, and evaluate impact. What You’ll Do - Lead, develop, and grow a team of Product Analysts and/or Data Scientists, ensuring consistently high performance, strong technical standards, and clear ownership of impact. - Drive effective goal-setting, planning and execution processes across Product Data Science, bringing leadership and discipline to OKRs, prioritisation and delivery against strategic objectives. - Set and continuously raise the bar for analytical quality, experimentation rigour, and data science application across your teams, ensuring outputs are robust, actionable, and decision-oriented. - Act as a senior technical and strategic leader, reviewing and shaping high-impact analytical work, experimentation design, and advanced modelling approaches where required. - Partner closely with senior Product, Engineering, Marketing, and Commercial leaders to define priorities, shape roadmaps, and ensure data science is embedded in strategic decision-making. - Translate ambiguous business problems into structured analytical and data science problems, ensuring your team delivers clear, commercially meaningful recommendations. - Drive adoption of scalable analytical frameworks, experimentation standards, and AI-enabled tooling to improve efficiency, consistency, and speed of decision-making across teams. - Champion best practices in experimentation, causal inference, segmentation, and customer understanding, ensuring statistical and analytical rigor across the organisation. - Build and maintain strong partnerships with Data Platform, Data Engineering and other central data functions, ensuring the team can effectively leverage shared capabilities while influencing the long-term data ecosystem. - Build and evolve the team’s capability through hiring, coaching, and performance management, ensuring strong technical depth and leadership within the function. - Identify and remove systemic blockers to high-quality analytics delivery, improving tooling, processes, ways of working and organisational effectiveness across Product Data Science while leading change that enables the team to scale. - Influence and align cross-functional stakeholders across multiple product domains, ensuring clarity, prioritisation, and strong decision-making discipline. Qualifications - Extensive experience in data science or a similar quantitative role, with a proven track record of supporting and influencing a product organization. - Expert Level proficiency in Python and SQL. - Deep, hands-on experience with statistical modeling, (quasi) experimentation, multi-arm bandit, and a wide range of machine learning techniques (e.g., Regression, Classification, Clustering). - Demonstrated ability to define, implement, and operationalise crucial product and feature-level metrics from scratch. - A proven track record of driving strategic impact through proactive and collaborative approach with the proven ability to lead technical discussions, drive product strategy, and communicate complex insights effectively to cross-functional partners (e.g., Product, Engineering, Design). - Experience scaling analytics or data science capabilities, driving impact through the creation of automated processes, self-service tools, or data products. - Leader in critical thinking, with previous experience demonstrating the analysis of available facts, evidence, observations, and arguments in order to form a judgment by the application of rational, skeptical, and unbiased analyses and evaluation. - Outstanding leadership skills, with experience in mentoring, coaching, and developing teams of analysts or data scientists. - Exceptional collaboration and communication skills, with the ability to engage, influence, and inspire cross-functional partners at all levels. - Proven ability to build strong relationships and drive outcomes across Product, Engineering, Data Platform and other central functions, often without direct authority. - Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field. Requirements - Experience working within a high-scale technology company, marketplace, e-commerce business, or travel technology organisation. - A strong technical background in Product Data Science, Data Science, Experimentation, or Machine Learning before moving into leadership roles. - Experience building and scaling experimentation platforms, measurement frameworks, self-service capabilities, or data products. - Experience applying AI, Large Language Models (LLMs), Agentic AI, or automation technologies to improve analytics productivity and decision-making effectiveness. - Experience leading organisational change, improving analytical maturity, and raising standards across multiple teams or functions. - A reputation for raising the standard of thinking, execution, and decision-making in every team and organisation you join. Benefits

Role Description ICF is seeking a well-organized and detail-oriented candidate with public health data analysis experience to support federal public health evaluation projects. This role will work remote in the Atlanta, Georgia area. Key Responsibilities: - Conduct advanced analysis of public health data (quantitative and qualitative) to support program evaluations. - Provide analytic guidance to program leadership and other agency representatives. - Contribute to the development of reports, manuscripts, briefing materials, etc. Qualifications - Masters Degree in public health, statistics, epidemiology, public administration, health policy, data analytics, economics, or related field. - At least 5 years of experience analysis of data within public-sector, healthcare, or grant-funded environments. - Experience with government-funded public health program evaluations. - Expert in Microsoft Office 365 and Windows, including experience using Excel. - Experience using qualitative analysis software (e.g., MAXQDA, ATLAS.ti). - Experience with using a statistical package (e.g., R, Stata, SAS). - Experience with CDC or other federal health agencies. - This position requires a HHS (Health and Human Services) background check. Applicants will be subject to a government security investigation to obtain clearance prior to start date. Requirements - Professional or educational experience in diabetes or other chronic disease prevention. - Experience with CDC or other federal health agencies. Benefits - Reasonable Accommodations are available, including, but not limited to, for disabled veterans, individuals with disabilities, and individuals with sincerely held religious beliefs, in all phases of the application and employment process. - Pay Range: $61,232.00 - $104,094.00 based on full-time employment.

• Partner with operations leaders to identify data visibility gaps and opportunities • Build scalable reporting that measures team performance and SLA-adherence in real time • Conduct root cause analyses and deep dives to understand drivers of operational inefficiencies • Advance the maturity of the team and organization through data modeling, development of best practices and frameworks, and leveraging AI capabilities for stakeholder self-service

• Define and lead the advanced analytics capability roadmap, aligning analytics investments with priorities across Commercial, Medical Affairs, and Clinical Operations and measurable enterprise outcomes. • Develop patient journey and evidence-oriented analytics using longitudinal healthcare data, claims, prescription, specialty pharmacy, EHR, clinical trial, real-world data, and other relevant data sources to identify key moments of intervention, access barriers, adherence challenges, scientific insights, and CT acceleration. • Design and scale analytics solutions that support omnichannel engagement, customer segmentation, campaign measurement, field effectiveness, medical engagement, and CT acceleration. • Lead the development and deployment of predictive models, machine learning capabilities, next-best-action solutions, and decision-support tools that enable more personalized, evidence-based, and effective engagement and operational planning. • Shape and evangelize the organization’s AI strategy for analytics, including practical use cases for generative AI, agentic AI, natural language interfaces, intelligent automation, and decision intelligence across Commercial, Medical Affairs, and Clinical Operations.