• Own the end-to-end data science lifecycle for moderately complex models and significant project components — spanning data ingestion, feature engineering, modeling, validation, deployment, monitoring, and retraining. • Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design), selecting appropriate methods for complex data science problems. • Write efficient, modular, well-tested code for data processing, feature engineering, and model training/inference, leveraging distributed tooling (e.g., Vertex AI pipelines, Dataflow, BigQuery) where appropriate. • Design and implement robust validation frameworks for complex experiments and models, accounting for potential biases and real-world performance. • Troubleshoot complex model performance issues, data anomalies, and code bugs effectively with little guidance. • Define analytical approaches and scope data science projects for moderately complex or ambiguous business problems. • Partner with product managers and stakeholders to define success metrics and experiment goals, and to translate marketplace problems into data science solutions. • Lead the design and analysis of experiments (e.g., A/B tests, switchback) for your projects, and interpret complex model results and experimental outcomes with a focus on actionable insights and business outcomes. • Proactively identify opportunities within your domain where data science can provide significant value, and initiate exploration. • Follow and help improve established team processes for coding standards, documentation, reproducibility, and experimentation. • Mentor DS I and DS II scientists, providing technical guidance, reviewing code, analyses, and models, and supporting their growth in analytical and modeling skills. • Influence technical decisions within the team regarding modeling choices, validation strategies, and tooling through well-reasoned arguments and expertise. • Drive improvements to team standards, data science best practices, and analytical rigor; take ownership of specific team practices or technical components (e.g., a feature store component, leading experimentation reviews). • Educate stakeholders on the capabilities and limitations of data science models, and clearly explain complex methodologies and findings to both technical and non-technical audiences. • Participate actively in recruiting, providing high-quality, graded interview feedback for candidates up to this level.

Role Description We are seeking a creative and passionate Head of Data Science to join our team. You will work closely with our Product, Engineering, Growth, Finance, Clinical, and Ops teams to transform behavioral and engagement data into strategic insight that shapes how members experience and benefit from Function Health. The ideal candidate is a product-centric data scientist. A leader who deeply understands the levers that drive member signups, engagement, and retention in consumer apps, and who can translate that understanding into clear product direction. You are comfortable working through ambiguity, meticulous in your attention to detail, self-motivated, and capable of thriving in a fast-paced, high-growth environment. As the leader of our Data Science team, your contributions will play a critical role in shaping the future of our member experience as we scale from several hundreds of thousands of members to many millions. This role is best suited for those energized by fast-paced environments, excited to build in a high-growth setting, and deeply motivated by our mission at Function. If you're looking for meaningful challenges, dynamic work, and the opportunity to make a real impact - we’d love to meet you. Key Responsibilities - Lead, manage, and develop a high-performing Data Science team, including coaching, performance management, career development, hiring, leveling, and team operations. - Set the Data Science strategy and roadmap across Function’s consumer business, spanning acquisition, activation, engagement, retention, member health outcomes, marketing, finance, operations, and product experience. - Partner with Product, Engineering, Growth, Marketing, Clinical, Finance, and Operations leaders to prioritize the highest-impact DS work and ensure the team is focused on the most important company outcomes. - Build the operating model for the DS function, including intake, prioritization, stakeholder management, project planning, quality standards, and communication cadences. - Establish and evolve Function’s core product and business measurement frameworks, including north-star metrics, funnels, retention, engagement, member value, experimentation standards, and executive reporting. - Raise the quality of company decision-making by ensuring analyses are rigorous, actionable, clearly communicated, and tied to concrete product, operational, or business decisions. - Guide the team in applying the right analytical methods to the right problems, including experimentation, causal inference, segmentation, forecasting, behavioral analysis, personalization, and measurement of member health-related outcomes. - Partner with Data Engineering, Analytics Engineering, and Product Engineering to improve the reliability, usability, and governance of Function’s data foundations while enabling the DS team to move quickly. Qualifications - 10+ years of experience in data science, product analytics, or applied analytics, including significant experience in consumer technology, subscription, marketplace, fintech, health-tech, or another high-scale digital product environment. - 4+ years of people-management experience, including direct management of data scientists or product analysts; experience leading a team of 15+ is strongly preferred. - Proven ability to build, lead, and scale a Data Science function, including hiring, coaching, performance management, team structure, planning, prioritization, and operating cadences. - Strong product and business judgment, with a track record of using data to influence roadmap decisions, growth strategy, retention strategy, operational performance, and executive-level tradeoffs. - Deep fluency in product and business analytics, including funnels, retention, engagement, cohort analysis, segmentation, experimentation, causal inference, metric design, and executive reporting. - Excellent executive communication and stakeholder management skills, with the ability to translate ambiguous business problems into analytical roadmaps and clear decisions. - Strong technical fluency in modern data science and analytics workflows, including SQL, statistical reasoning, experimentation, and analytical tooling. - Nice to have: experience with subscriptions, personalization, lifecycle growth. Requirements - Ruthless Prioritization: We don’t let perfect get in the way of progress. We move quickly to drive value, not perfection. We prioritize what drives impact. We never compromise on standards of excellence. - Member-First, Always: We design and deliver like we’re caring for someone we love. We create calendar, actionable, human experience. We prioritize responsiveness, peace of mind, and outcomes. We empower members with truth, clarity, and care. - One Team, Moving Fast: We are aligned in purpose, prioritization, and speed. We gather diverse perspectives to make informed decisions. We clear paths for each other and move fast together. We communicate clearly and respectfully, rallying around shared goals. - Radical Ownership, Relentless Execution: We don’t just ship– we own outcomes and drive results. We act with urgency and precision. We anticipate, initiate, and follow through. We meet challenges with grit and pragmatism. We embrace new tech to deliver better outcomes. - Mission Over Ego: We are ruthlessly aligned to our mission– and leave ego at the door. We disagree and commit. We don't tolerate politics or withholding information. We operate with honesty, transparency, and respect. - Sustained Integrity in Every Detail: We earn trust by obsessing over accuracy, quality, and clarity in everything we do. We prioritize clinical precision– data must be right. We sweat the details because outcomes depend on them. Benefits - Competitive salary and benefits package. - Flexible working hours. - A dynamic work environment where creativity and innovation are encouraged.

• Design and analyse A/B tests across Viator’s marketplace, applying sound statistical methods to interpret results and support confident decision-making • Champion experimentation best practices: power calculations, guardrail metrics, and multiple testing corrections. • Develop your mastery of causal inference and advanced experimentation techniques (e.g., difference-in-differences, propensity scoring, synthetic controls) as you apply them to answer questions that can't be randomized • Own the measurement framework for your product area: define key metrics, build the instrumentation to track them, and surface insights that move the needle. • Conduct exploratory analyses and deep dives into our data, using various data science approaches, to inform product decisions • Enable self-service through scalable datasets, metrics, dashboards and reporting frameworks • Translate analytical outputs into actionable insights and clear product recommendations • Act as a thought partner with product managers and engineers to ensure the right data questions are being asked • Translate analyses into clear narratives that are accessible to non-technical audiences

Role Description Data Scientist - positions offered by Ford Motor Company (Dearborn, Michigan). Note, this is a purely telecommuting/work-from-home position whereby the employee may reside anywhere within the U.S. - Leading the development and delivery of a suite of GenAI applications in support of the global marketing organization. - Architecting and integrating complex novel data science algorithms into the fabric of these end user applications. - Ensuring application performance and adhering to enterprise compliance and best practice development standards. - Mentoring junior data scientists. Qualifications - Master’s degree or foreign equivalent in Data Science, Computer Science, Statistics, Operations Research, Mathematics, Marketing Science, Industrial Engineering, Computer Applications or a related field. - 5 years of experience in the job offered or a related occupation. - 5 years of experience with Python and SQL programming for data extraction, analysis, or integration. - 5 years of experience using cloud platforms (Google Cloud Platform, AWS, or Azure) and building data processing pipelines (Spark and Dataflow) and utilizing managed machine learning services. - 5 years of experience in A/B testing design, implementation, and analysis, and experimental design methodologies. - 5 years of experience with MLOps practices, including model deployment, versioning, and automated monitoring in production environments. - 4 years of experience in data architect and GenAI pipeline development using OpenAI, LLaMA, Gemini, Vertex AI, or AutoML. - 4 years of experience in advanced statistical modeling, machine learning techniques, including regression, classification, forecasting, and causal inference, using relevant data science libraries and frameworks (Scikit-learn, XGBoost, TensorFlow/PyTorch, or Pandas). - 3 years of experience in the development of GenAI and machine learning models. Requirements - Salary of $128,710.00 - $186,624.00/yr. Benefits - Immediate medical, dental, and prescription drug coverage. - Flexible family care, parental leave, new parent ramp-up programs, subsidized back-up child care and more. - Vehicle discount program for employees and family members, and management leases. - Tuition assistance. - Established and active employee resource groups. - Paid time off for individual and team community service. - A generous schedule of paid holidays, including the week between Christmas and New Year's Day. - Paid time off and the option to purchase additional vacation time.