Sr Analyst, Data Science

Why project44?   At project44, we believe in better.   We challenge the status quo because we know a better supply chain isn’t just possible—it’s essential. Better for our customers. Better for their business. Better for the world.   With our Decision Intelligence Platform, Movement, we’re redefining how global supply chains operate. By transforming fragmented logistics data into real-time, AI-powered insights, we empower companies to connect instantly, see clearly, act decisively, and automate intelligently. Our Supply Chain AI enhances visibility, drives smarter execution, and unlocks next-gen applications that keep businesses moving forward.   Headquartered in Chicago, IL with a 2nd HQ in Bengaluru, India we are powered by a diverse global team that is tackling the toughest logistics challenges with innovation, urgency, and purpose.   If you’re driven to solve meaningful problems, leverage AI to scale rapidly, drive impact daily, and be part of a high-performance team – we should talk.  Description:     project44 is looking for a Sr Analyst, Data Science to join our engineering team. You will work in a fast-paced Agile environment designing, building, and implementing best-in-class integrations to accelerate how project44 connects to the world’s logistics networks.    About the Role We are looking for a Senior Product Analyst, Data Science, to drive analytics for ML-powered products at project44. This role sits at the intersection of Product, Data Science, and Operations, with a mandate to define how ML systems are measured, evaluated, and improved in production. You will own the analytical frameworks that connect model performance to product outcomes, ensuring our AI capabilities deliver measurable business impact. You will work on ambiguous, high-impact problems, designing experiments, evaluating models (offline and online), and shaping product strategy through data. This is not a reporting role. You are expected to operate as a thought partner to Product and Data Science, influencing decisions and driving outcomes. As analytics evolves, this role will focus on building AI-native and agent-driven analytics systems, including semantic layers, data contexts, and automated insight generation. You will help define how analytics is consumed in an environment where agents, not just dashboards, drive decisions. What You’ll Do Own Product & ML Measurement Define and operationalize success metrics across product and ML systems, including north-star metrics, product KPIs, and model-level evaluation frameworks. Drive Model Evaluation & Feedback Loops Evaluate model performance using offline and online metrics (e.g., precision/recall, lift, latency, adoption) and establish feedback loops to continuously improve models in production. Translate Product Problems into Analytical Frameworks Break down ambiguous product and operational problems into structured analyses that drive clear, actionable decisions. Partner Across Product & Data Science Work closely with Product Managers and Data Scientists to shape roadmaps, guide prioritization, and ensure alignment between model performance and user impact. Build Scalable Analytics Foundations Develop reusable datasets (SSOTs, data marts) and enable self-serve analytics across teams. Build AI-Native Analytics Systems Design and develop semantic layers, data models, and context systems that enable agent-driven analytics. Build and operationalize workflows where agents autonomously generate insights, monitor performance, and surface recommendations on a recurring basis. Design Agent-Driven Analytics Workflows Design and implement agent-driven analytics workflows where insights are generated, monitored, and delivered autonomously using LLMs and modern AI tooling. What We’re Looking For 4–6+ years in Product Analytics, Data Science, or related roles, with experience supporting data-driven product decisions. Analytical & Statistical Expertise Strong foundation in statistics and experimentation, including hypothesis testing, A/B testing, and causal inference. Technical Skills Proficiency in SQL and Python, with experience working with large-scale, event-level datasets and modern data platforms. ML Product Understanding Experience defining and analyzing model performance metrics and understanding trade-offs between offline and online evaluation. Cross-Functional Collaboration Proven ability to work closely with Product and Data Science teams to drive outcomes. Communication & Influence Ability to clearly communicate complex analyses and influence stakeholders across technical and non-technical audiences. Preferred Skills Experience working on ML-powered products or data platforms Familiarity with model evaluation concepts (precision/recall, ROC, calibration, bias/variance) Experience analyzing online vs offline performance and feedback loops Familiarity with modern data stack (dbt, Snowflake/BigQuery, event tracking, feature stores) Experience with predictive modeling or close collaboration with Data Science teams Hands-on experience using AI tools (LLMs, copilots) in analytical workflows Experience building or working with agent-based or AI-native analytics workflows Familiarity with semantic layers, metrics layers, or data context systems Experience using LLMs for automated analysis, insight generation, or decision support Strong product and business intuition What Success Looks Like (6–12 Months) Establish clear measurement frameworks for assigned ML-powered products Improve visibility into model performance and diagnostics (offline and online) Deliver actionable insights that influence product roadmap decisions for assigned products Build scalable datasets and frameworks that enable self-serve analytics Enable tighter feedback loops between Product, Data Science, and real-world outcomes In-office Commitment: This position requires a commitment to contribute to our collaborative culture by working in-office three days weekly.  Diversity &