Data Scientist, Forecasting and Optimisation

Merchmix; Hinoba-an; On-site; PHP 995,000 - 1,393,000; Full time

Job summary

Merchmix is seeking a Data Scientist for Forecasting and Optimisation to design and deploy models that drive stock moves, collaborating with product, engineering and customer-facing teams. You will build forecasts at SKU/store/week levels, develop optimisation and simulation models, and contribute to the reasoning layer of autonomous workflows with clear explanations and robust evaluation.

You will ensure model outputs write back into ERPs to support automated purchasing decisions and monitor

Qualifications

• 3–5 years building and deploying machine learning in production.
• Depth in at least two of forecasting, optimisation, simulation, or causal inference.
• Genuine software engineering ability with tested, maintainable code.
• Evidence you learned something hard and shipped it.
• Strong SQL with window functions and query plans.
• Experience with enterprise data: reconciled SAP/NetSuite/Dynamics data.
• Clear communication with non-technical stakeholders.
• Experience with LLM systems in production, especially evaluation and guardrails.

Responsibilities

• Forecast demand at SKU, store and week level, including cold start on new product.
• Build hierarchical reconciliation so forecasts sum coherently to regional and national plans.
• Develop optimization models for allocation, replenishment, safety stock, assortment and markdown timing.
• Create the simulation layer behind scenario tools for credible what-if answers.
• Work on the reasoning layer behind autonomous workflows and forecast explanations.
• Measure causal effects: promotional lift and price elasticity.
• Set standards for backtesting, baselines, error metrics, data quality checks and auditability.

Skills

Forecasting

Optimization

Simulation

Causal inference

SQL

Rust

Software engineering

Communication

LLM in production

Tools

Rust

Job description

Data Scientist, Forecasting and Optimisation

Merchmix is a merchandising and inventory operating system with intelligence at its core, helping retailers connect planning, buying, allocation, suppliers and execution in one platform. From fashion to FMCG, retailers use Merchmix to cut waste, protect margin and decide faster. We want to change how retail is run.

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 shipped 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 shipped 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.