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Accrete believes in being a company that provides benefits and happiness to all of its employees. We foster an environment that appreciates each employee's effort and ensures that their worth is appropriately acknowledged through the best incentives, facilities, and career opportunities.

Innovation through continuous leaning and implementation is the key to our success. The pleasure factor of our staff is reflected in their effectiveness, and hence in Accrete's ever-growing success. If you, too, want to join the Accrete family and earn competitive remunerations, intensive training, and a sense of importance, please fill out the form below and we will contact you as soon as possible.

About Us

Data Scientist / ML Engineer (2-6 Years) (Full Time)

Office No-1, IT Tower-1, Ground Floor, Infocity, Gandhinagar, Gujarat 382007

We're looking for a Data Scientist / ML Engineer to build demand and sales forecasting capability for our enterprise supply-chain and distribution planning products. You'll work on real production forecasting problems — predicting product-level demand across a distribution network, handling seasonality and promotional effects, and solving for products with little or no sales history — and you'll be responsible for getting those forecasts into the hands of planners through the applications our engineering team builds. This is a hands-on, build-from-scratch role: there's no existing forecasting pipeline to inherit. You'll define the approach, build it, and iterate with direct client feedback.

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As a Data Scientist / ML Engineer, you'll need to:

  • Design and build demand/sales forecasting models (time-series methods such as ARIMA/SARIMA, exponential smoothing, Prophet, as well as ML-based approaches like LightGBM/XGBoost) for distribution and replenishment planning use cases.
  • Handle real-world forecasting challenges: seasonality, promotional and scheme effects, and cold-start forecasting for newly launched products with no sales history.
  • Build data pipelines to ingest and clean sales, stock, and order data from enterprise ERP systems (SAP experience is a strong plus) and structure it for modeling.
  • Integrate forecast outputs into downstream planning applications via APIs or scheduled jobs, working closely with backend developers.
  • Collaborate with business analysts and client stakeholders to translate supply-chain domain rules and constraints into model features.
  • Track and report model accuracy (e.g., MAPE, bias), and iterate models based on real business outcomes, not just offline metrics.
  • Build simple dashboards/reports to explain forecast performance to non-technical business stakeholders.
  • Document your modeling approach and keep pipelines reproducible and maintainable by the rest of the team.

Education & Experience Requirement

  • Bachelor's or Master's degree in Computer Science, Statistics, Data Science, or a related field.
  • 2–6 years of experience in data science or ML, including at least one real-world forecasting or time-series project (academic projects alone are not sufficient).
  • Strong Python skills — pandas, numpy, scikit-learn — and familiarity with forecasting libraries such as Prophet, statsmodels, or equivalent.
  • Solid grasp of time-series fundamentals: trend, seasonality, and how to approach cold-start/no-history forecasting problems.
  • Working SQL skills and comfort querying relational databases directly.
  • Experience building or consuming APIs to get model outputs into real applications — this role isn't notebook-only.
  • Comfortable working directly with client stakeholders and explaining modeling decisions in plain business language.
  • Comfortable in a small-team, multiple-hats startup environment.

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