• Design and implement supervised and unsupervised ML models (e.g., OLS, Logistic Regression, Ensemble Methods) to solve real-world business problems. • Lead model development for advanced architectures in neural networks, such as ANN, CNN, RNN, GAN, Transformers, and RESNet. • Drive NLP advancements with tools like NLTK and neural-based language models. • Oversee the development of computer vision models, utilizing OpenCV for real-world applications. • Lead time-series modeling projects for forecasting and anomaly detection. • Utilize AI techniques like Retrieval-Augmented Generation (RAG), Chain of Thought (CoT), and Model of Alignment (MOA) to enhance model performance. • Build and maintain scalable data pipelines for both streaming and batch processing. • Architect and optimize lakehouse solutions using Delta/Iceberg and bronze-silver-gold architectures. • Lead the development of ETL processes with tools like Airflow, DBT, and Airbyte to support data flow and transformation. • Design and optimize database models for OLTP and OLAP systems using Snowflake, SQL Server, PostgreSQL, and MySQL. • Develop NoSQL solutions, leveraging MongoDB, DynamoDB, and ElasticSearch for unstructured data. • Lead efforts in building cloud infrastructure, particularly in AWS (preferred) or Azure, using services such as Lambda, API Gateway, Batch processing, Kinesis, and Kafka. • Oversee MLOps pipelines for robust deployment of ML models in production with platforms like Sagemaker, Databricks, and Azure ML Studio. • Develop and optimize business intelligence dashboards with tools like Tableau, QuickSight, and PowerBI for actionable insights. • Implement GPU acceleration and CUDA for model training and optimization. • Mentor junior team members in cutting-edge AI/ML techniques and best practices.

Role Description The Strategic Data Scientist will partner with operational, planning & procurement and finance partners to understand what is happening within the business so solutions can be developed to unlock strategic insights and then carry forward these insights to development of transformation plans that would be implemented (in working together with a project manager / operations leader). As a Strategic Data Scientist they will spend significant time developing strategic data insights to advance initiatives in efficiency, agility, cost optimization through customer-centric and operations-led settings. In addition, this role will partner with the purchasing & planning organization to develop insights to demand trends. - Undertakes research and analysis to evaluate consumer demand patterns, develop insights, work with executive team to undertake demand outlook analysis and determine inputs to purchasing plans so as to manage inventory and guide commercial & supply chain operations. - Focuses on leveraging data to optimize operations, improve efficiency, and drive business strategy through specific strategic projects. - Analyzes complex datasets to identify trends, patterns, and areas for improvement within the distribution network, and translate these findings into actionable insights and recommendations. - Develops / works with project management / operations leadership / commercial leadership to develop transformation plans and undertake change management. - Utilizes scenario modeling and predictive analytics to support strategic planning, conducts root cause analysis to support continuous improvement initiatives and monitors the effectiveness of implemented strategies and adjust based on performance data. Qualifications - 5 to 7 years of experience in Commercial & Strategic Finance, Supply Chain Finance, FP&A or related fields. - Bachelor's degree in Computer Science, Business, Data Analytics, Engineering, Finance, Economics, or equivalent experience required. - Prior experience working directly with one or more of the following domain areas – commercial & sales, supply chain or growth & transformation. - Experience creating dashboards and working in Power BI. - Bonus: Graduate degree in statistics, business, economics, econometrics, supply chain or related discipline. Requirements - Proficiency in statistical analytical software such as R, SAS, or SPSS, as well as database tools like SQL, MS Access, Excel PivotTables, and MS SQL Server. - Experience using and creating dashboards in visualization tools such as Python, Power BI, Tableau, and QlikView. - Expertise in MS Office suite, including Excel, Access, and PowerPoint. - Strong commitment to meeting deadlines and project schedules. - Present findings and actionable insights to both technical and non-technical stakeholders. - Support cross-functional teams with data-driven recommendations. - Comfortable analyzing data sets and working in data-sparse environments. - Excellent communication and presentation skills, including the ability to interact with senior executive management regularly. - Adaptability to change in a fast-paced and constantly evolving environment. - Effective project management skills. - Extreme attention to detail. - Highly motivated self-starter with a passion for making high quality analytical solutions. - Committed to a great user experience. - Strong sense of ownership and drive. - Positive, enthusiastic attitude with ability to interact with cross-departmental groups. Work Environment/Physical Demands - Typical office environment – moderate noise level. - This position regularly requires standing; walking; sitting; use hands; reaching with hands and arms; and talking and/or hearing. - Occasional lifting and/or moving up to 25 pounds. We are an equal opportunity employer, dedicated to fostering a diverse and inclusive workplace where everyone is valued and has equal access to opportunities. WE ARE A DRUG FREE WORKPLACE.

• Lead, mentor, and grow a high-performing team of data scientists; set clear priorities, uphold technical excellence, and develop career paths. • Define and own the analytics and experimentation strategy across storefront, browse/aisles, search, cart, checkout, OSP, Family, Lists, and Meals/Health—covering metrics, guardrails, instrumentation, and experiment best practices. • Own core shopping metrics and event logging; improve data quality, build reusable dashboards/tools, and ensure reliable, timely insights for decision-making. • Drive “DS understand projects” that uncover friction in shopping funnels; scope root-cause analyses and partner with PM and Eng to prioritize and ship fixes that move conversion and retention. • Partner as a thought leader with Product and Engineering leadership to shape roadmaps, make tradeoffs across Enterprise, Lifecycle, Category Growth, and Foundations work, and ensure goals are measurable and achievable. • Set the bar for experiment design and readouts; coach teams on hypothesis formation, sampling, power analysis, metric selection, and clear storytelling of results and implications. • Collaborate with ML partners on ranking, recommendations, and personalization initiatives, aligning offline/online evaluation with business outcomes and shopper experience goals. • Influence and improve cross-functional rituals (e.g., experiment reviews, prioritization forums) to increase speed, rigor, and learning across the organization. • Ensure AI/agentic features are grounded in robust data and measurement frameworks, with clear definitions of success and long-term impact.

• Lead the data science and analytics strategy for underwriting transformation software products, from problem framing through production deployment. • Design and operationalize causal, predictive, and scenario-based analytical capabilities supporting underwriting, pricing, and risk decisions. • Partner closely with Product Management and Engineering to embed analytics and reporting into core product workflows. • Define, track, and report product, operational, and business metrics and OKRs aligned to organizational and underwriting transformation goals. • Establish scalable reporting frameworks and dashboards that provide transparency into product performance, financial impact, and delivery outcomes. • Deliver executive-ready insights that clearly articulate progress, risks, and tradeoffs. • Provide technical leadership in causal inference and counterfactual analysis to assess underwriting and pricing changes. • Ensure analytical models are interpretable, validated, and production-ready. • Facilitate continuous, cross-functional communication across Product, Engineering, Underwriting, Finance, Actuarial, and Executive teams. • Build trusted partnerships with stakeholders at all organizational levels, serving as a strategic thought partner for data-driven decisions.