Manager, Data Scientist

Manager, Data Science

Retail and Consumer Packaged Goods (CPG) supply chains operate in dynamic, high-stakes environments where every decision—from what to stock to how to price—hinges on knowing what customer demand will look like tomorrow.

Our team delivers enterprise-grade SaaS decision management solutions on Microsoft Azure and Databricks. At the foundation of our platform is a cutting-edge Time Series Demand Forecasting engine, which directly powers downstream optimization applications including:

• Inventory Allocation & Replenishment
• Assortment Planning & Store Clustering
• Initial Pricing, Promotions, and Markdown Optimization
• Predictive Ordering, and Size & Pack Optimization
• Intelligent Order Promising
• Retail Shrink, Fraud, & Supply Chain Anomaly Detection
• Workforce & Labor Scheduling Optimization

As the Manager, Data Science, you will occupy a high-impact leadership role at the intersection of people mentorship, customer engagement, technical architecture, and GenAI transformation. You will lead a dedicated pod of data scientists, own enterprise client relationships from an analytical standpoint, shape the strategic roadmap of our core SaaS products, and spearhead the adoption of GenAI, LLMs, and Agentic AI to drastically accelerate team productivity and drive scalable professional services delivery.

Key Responsibilities & What You'll Do:

• People Leadership & Talent Development
• Lead & Mentor a High-Performing Pod: Directly manage, mentor, and coach a focused team of 2–3 junior/associate data scientists and interns.
• Technical Guidance & Quality Standards: Conduct rigorous code, model, and methodology reviews to ensure high analytical fidelity, production-readiness, and adherence to ML best practices.
• Career Growth: Set clear objectives, conduct performance reviews, foster a culture of continuous learning, and help team members rapidly upskill across our cloud lakehouse and AI-native SaaS ecosystem.
• Customer Engagement & Professional Services Delivery
• Lead Client Analytics Engagements: Act as the primary data science leader and trusted advisor for enterprise retail/CPG customer stakeholders (Directors, VPs of Supply Chain, Merchandising, and IT).
• Scope & Expectation Management: Translate ambiguous customer business problems into concrete mathematical and data science roadmaps; scope deliverables, negotiate commitments, and proactively manage project timelines and expectations.
• Executive Storytelling & Value Delivery: Communicate complex predictive and prescriptive model behaviors (e.g., promotional lift, price elasticity curves, allocation trade-offs) in plain, business-centric language that demonstrates clear ROI.
• GenAI & Agentic-AI Operational Transformation
• Drive Professional Services Scalability: Lead the exploration, design, and internal rollout of GenAI and Agentic-AI capabilities to 10x the productivity and delivery speed of our professional services organization.
• Automate Core Data & Deployment Workflows: Architect agentic workflows for autonomous data discovery, schema mapping, and ETL migration, drastically reducing customer onboarding lead time.
• Innovate Frontier AI Capabilities: Spearhead the application of LLMs and foundation models to solve complex domain challenges—such as automated forecast validation, natural-language root-cause generation, promotional cannibalization modeling, and "cold-start" demand intelligence for zero-history new product or new store launches.
• SaaS Product Innovation & Thought Leadership
• Bridge Services to Product: Identify recurring customer requirements and algorithmic customizations, partnering closely with Product Management and Core Engineering to productize them into standard, reusable SaaS platform features.
• Algorithmic & Architecture Leadership: Provide hands-on architectural direction across statistical baselines, gradient boosting (XGBoost, LightGBM, CatBoost), deep learning/neural network models (TiDE, DenseNet), foundation time-series models (TimesFM, Chronos), and mathematical optimization algorithms.

Minimum Qualifications:

• Education: Master's or Ph.D. (or equivalent practical experience) in Data Science, Computer Science, Operations Research, Statistics, Industrial Engineering, Applied Mathematics, or a related quantitative field.
• Professional Experience: 8+ years of progressive data science experience, including at least 1–2 years of demonstrated technical team mentorship, leadership, project / program management, or direct people management.
• Client Engagement & Stakeholder Mastery: Proven track record in customer-facing analytics delivery (e.g., enterprise SaaS professional services, management/analytics consulting, or internal corporate stakeholder management).
• Core Technical Depth:
• Strong expertise in time series forecasting, classical statistical modeling (hierarchical forecasting, intermittent demand, temporal sequence modeling), machine learning, and deep learning/neural networks
• Deep proficiency in Python, SQL, and standard data science packages (pandas, numpy, scikit-learn).
• Hands-on experience with cloud lakehouse architectures (Databricks, Azure, PySpark, Delta Lake, Unity Catalog, MLflow).
• GenAI / LLM Practical Acumen: Working knowledge of modern GenAI paradigms (prompt engineering, RAG, tool calling, multi-agent frameworks like LangChain/AutoGen/CrewAI) and a clear vision for how to apply them to operational workflows.
• Executive Communication: Exceptional verbal and written communication skills with the ability to bridge deep mathematical concepts with C-suite business strategy.

Preferred Qualifications:

• Prior experience in Retail, CPG, or Supply Chain Management decision systems (e.g., inventory replenishment, price elasticity, markdown optimization, assortment planning).
• Strong background in Operations Research & Mathematical Optimization (Linear/Mixed-Integer Programming with PuLP, SciPy.optimize, OR-Tools, or Gurobi).
• Experience productizing custom professional services algorithms into reusable multi-tenant SaaS modules.

What We Offer:

• High-Visibility Leadership: Serve as a core technical and client-facing leader shaping the AI direction of an enterprise SaaS platform.
• Bleeding-Edge Tech Stack: Build on modern enterprise infrastructure combining Databricks Lakehouse, state-of-the-art time series foundation models, and custom Agentic-AI pipelines.
• Impact & Scalability: Direct ownership of strategies that improve multi-million dollar supply chain efficiencies for global retail brands while transforming our internal delivery economics.

Benefits:

We understand the importance of work-life balance and wellbeing, which is why we offer flexibility for our teams including: hybrid work, adaptable hours, Summer Flex Fridays, Focus Fridays, and an annual companywide well-being day to promote revitalization and success.