Data Scientist, Forecasting and Optimisation

Merchmix; Bengaluru; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

merchmix is hiring a Data Scientist for Forecasting and Optimisation in Bengaluru. You will design and build models that decide stock movement, collaborating with product, engineering and commercial teams. Models progress from problem definition to prototype and production in Rust.

Key areas include forecasting demand, hierarchical reconciliation, allocation and replenishment optimisation, scenario tools, and autonomous workflow reasoning. Strong SQL and production ML experience are essential.

Qualifications

• 3–5 years building and deploying ML in production.
• Deep experience in at least two of forecasting, optimisation, simulation, causal inference.
• Genuine software engineering ability with maintainable code and ownership.
• Strong SQL and data wrangling with enterprise data (SAP/NetSuite/Dynamics).
• Experience with LLM systems in production and evaluation.

Responsibilities

• Forecast demand at SKU, store and week level with sparse data.
• Build hierarchical reconciliation for region and national plans.
• Develop optimisation models for allocation, replenishment and inventory policy.
• Create simulation layer for scenario tools and fast feedback.
• Support autonomous workflows with explainability and evaluation harnesses.
• Measure causal effects like promotional lift and price elasticity.
• Establish backtesting standards, baselines, and data quality checks.

Skills

Time series forecasting

Mathematical optimisation

Simulation

Causal inference

Software engineering

SQL

LLM systems in production

Tools

Rust

Job description

Data Scientist, Forecasting and Optimisation

merchmix is one platform for the whole of retail. From the first sketch to the last sale, retailers plan, buy, source, ship, sell and see it all in one place, live, with an agent called yukti that does the work. Founded in Sydney, August 2024, by people who ran planning and buying at M&S, ASOS, Debenhams and Best & Less.

This is a full time, on site role in Bengaluru. You will design and build the models that decide what stock moves, working with product, engineering and customer-facing teams. Models go from problem definition through prototype to production Rust.

Key responsibilities

• Forecast demand at SKU, store and week level, where most series are sparse and intermittent, including cold start on new product with no sales history.
• Build hierarchical reconciliation so store forecasts sum coherently to region and national plans, along with size and colour curves, cannibalisation and halo across a range, and seasonality that follows the retail calendar rather than the Gregorian one.
• Build the optimisation models behind allocation and replenishment, safety stock policy, assortment and range planning, and markdown timing and terminal stock risk.
• Build the simulation layer behind our scenario tools, so that when a merchandiser asks what happens if they shift ten per cent of stock online, something answers credibly and fast.
• Work on the reasoning layer behind our autonomous workflows: retrieval over retail context, tool use, natural-language explanation of forecasts, and the evaluation harnesses that tell us whether any of it works.
• Measure causal effects: promotional lift, price elasticity, and what would have sold without the discount.
• Set the standards for rolling-origin backtesting, honest baselines, error metrics that reflect commercial cost, data quality checks, model documentation and auditability. Our outputs write back into customer ERPs, so a retailer has to be able to trust an automated purchase order.

What we are looking for

• Three to five years building and deploying machine learning in production. What you delivered matters more than your title.
• Depth in at least two of time series forecasting, mathematical optimisation, simulation, or causal inference. All four is rare and we will not pretend otherwise.
• Genuine software engineering ability rather than scripting. Tested, reviewed, maintainable code, comfort with static types, and enough understanding of ownership, allocation and concurrency that Rust is new syntax rather than a new concept.
• Evidence you have learned something hard and unfamiliar fast and delivered with it. Tell us that story in your application, it is the thing we will dig into.
• Strong SQL. Window functions and query plans, not just joins.
• Experience with messy operational data from enterprise source systems: backdated corrections, missing weeks, hierarchies that change mid-year, schemas nobody documented. If you have reconciled data across SAP, NetSuite or Dynamics, say so.
• Clear communication with non-technical stakeholders. You will explain a forecast to a Head of Buying placing serious money into stock.
• Experience with LLM systems in production, particularly evaluation: eval sets, regression measurement, cost and latency management, and guardrails.