About Logile

Logile is the leading retail labor planning, workforce management, inventory management and store execution provider deployed in thousands of retail locations across North America, Europe, Australia, and Oceania.  

Our proven AI, machine-learning technology and industrial engineering accelerate ROI and enable operational excellence with improved performance and empowered employees. Retailers worldwide rely on Logile solutions to boost profitability and competitive advantage by delivering the best service and products at optimal cost.  

From labor standards development and modeling to unified forecasting, storewide scheduling, and time and attendance, to inventory management, task management, food safety, and employee self-service — we transform retail operations with a unified store-level solution. Gain the Advantage with The Logic of Retail. One Platform for store planning, scheduling and execution.  

For more information, visit www.logile.com 

Job Summary 

We are looking for a Lead Data Scientist with 8+ years of experience to head a team of applied scientists working on the quantitative problems that sit at the heart of retail operations. This is a hands-on leadership role for someone who can both set technical direction — spanning statistics, optimization, and machine learning — and stay close enough to the work to review a model, question an assumption, or unblock a stuck analysis. 

You will lead and grow a team of data scientists, set the technical roadmap for demand forecasting, price elasticity, and related pricing and planning models, and work closely with product, engineering, and operations leadership to turn quantitative insight into decisions retailers can act on. 

Key Responsibilities 

Team Leadership & People Management 

• Lead, mentor, and grow a team of data scientists — setting technical direction, reviewing modeling approaches, and raising the bar on statistical rigor across the team. 

• Own hiring, performance management, and career development for team members, building a group where both scientific depth and practical delivery are valued. 

• Prioritize and sequence the team's roadmap against business impact, balancing long-term research investments against near-term delivery commitments. 

• Act as the technical escalation point for the team's hardest modeling and statistical problems. 

Demand Forecasting & Price Elasticity 

• Own the technical strategy for demand forecasting and price elasticity modeling — the highest-priority quantitative use cases for the business — ensuring accuracy holds up under real-world variability. 

• Develop and oversee hierarchical and probabilistic forecasting models that capture seasonality, trend, and structural shifts across store, SKU, and category levels. 

• Guide the design of price elasticity and demand-response models that inform pricing, promotion, and markdown decisions, accounting for cross-item cannibalization and substitution effects. 

• Ensure models remain reliable through high-volume events, promotional periods, and other demand shocks, using appropriate uncertainty quantification and adaptive techniques. 

Statistical Analysis & Experimentation 

• Set standards for experimental design and causal inference across the team, drawing on both Bayesian and frequentist approaches depending on the problem. 

• Apply and oversee causal inference techniques where controlled experiments aren't feasible, and ensure assumptions and limitations are communicated honestly. 

• Direct deep-dive analyses on model performance, business KPIs, and forecast residuals, turning findings into concrete model or process improvements. 

• Maintain rigorous standards for uncertainty quantification, test design, and honest communication of what the data can and cannot support. 

Optimization & Decision Support 

• Lead the design of optimization models and algorithms that support pricing, assortment, replenishment, and scheduling decisions under real operational constraints. 

• Guide the team's use of constrained optimization, simulation, and heuristic methods to find solutions that are practical to implement at scale, not just theoretically sound. 

• Partner with operations and category leadership to understand decision workflows deeply enough to model and improve them meaningfully. 

Collaboration & Stakeholder Management 

• Partner with ML Engineering leadership to take models from research to production, ensuring they are reproducible, monitored, and degrade gracefully when inputs shift. 

• Represent the data science team to senior stakeholders — translating ambiguous business questions into well-specified modeling problems and communicating results and uncertainty clearly to non-technical audiences. 

• Build strong working relationships across product, engineering, and operations to keep the team's work aligned with business priorities. 

Job Location & Schedule 

This position is based onsite at the Logile Bhubaneswar Office. 

Candidates are expected to maintain working hours with meaningful overlap with US business hours (EST/CST), including overlap needed to manage a team and collaborate with US-based stakeholders. 

Required Skills & Experience 

8+ years in Data Science, Applied Statistics, or Quantitative Research roles, including experience leading or managing a team of data scientists. Prior experience in retail, CPG, or supply chain is strongly preferred. 

Leadership Skills 

• Demonstrated experience managing, mentoring, and growing a team of data scientists or quantitative researchers. 

• Track record of setting technical direction and prioritizing a roadmap against business impact. 

• Ability to communicate complex quantitative tradeoffs to senior, non-technical stakeholders and influence decisions accordingly. 

Quantitative Skills 

• Deep foundation in probability and statistics — comfortable with both Bayesian and frequentist frameworks, and able to choose between them based on the problem. 

• Strong background in time series modeling, hierarchical models, and probabilistic forecasting, particularly demand forecasting and price elasticity estimation. 

• Expertise in optimization methods and algorithms, including constrained optimization, mathematical programming, and simulation-based approaches. 

• Causal inference experience: experimental design, observational methods, and a clear understanding of where each breaks down. 

Retail & Domain Knowledge 

• Deep understanding of how retailers plan and operate — including planning cycles, operational constraints, and the seasonal, promotional, and external shocks that make retail data messy. 

• Hands-on experience with demand forecasting and price elasticity use cases, and the ability to connect model outputs directly to pricing and planning decisions. 

• Ability to reason about the downstream business consequences of model errors and to prioritize accordingly. 

Programming & Tools 

• Proficient in Python for data analysis and modeling (pandas, NumPy, scikit-learn, statsmodels, and Bayesian libraries such as PyMC or Stan). 

• Strong SQL skills for working with large transactional datasets. 

• Experience with experiment tracking, model versioning, and leading code-reviewed, collaborative engineering workflows. 

Preferred Skills 

• Prior experience as a team lead, manager, or principal-level individual contributor with informal leadership responsibilities. 

• Experience shipping models to production and maintaining them at scale — not just building them in notebooks. 

• Familiarity with distributed computing tools (Spark, Dask) for working with large retail datasets. 

• Exposure to reinforcement learning or multi-armed bandit approaches for pricing or decision-making under uncertainty. 

• Background in supply chain optimization or revenue management, even from adjacent industries. 

What Success Looks Like 

• The team's demand forecasting and price elasticity models are trusted and used in pricing and planning decisions — not just reviewed and shelved — because they are accurate, explainable, and fit the operational context. 

• Business teams have more confidence in their decisions, and a clearer view of the uncertainty around them. 

• The team catches problems before they become incidents: forecast failures, data drift, model degradation. 

• Data scientists on the team grow technically and professionally, and the team's output compounds because of how you lead. 

Compensation and Benefits 

Compensation and benefits for this role are benchmarked against the best in industry and are commensurate with experience and job location. 

• Standard shift: 1 PM – 10 PM (shift allowance applicable as per role). 

• Shifts starting after 4 PM: Eligible for food allowance/subsidized meals and cab drop. 

• Shifts starting after 8 PM: Eligible for cab pickup as well.