Data Scientist

As a Data Scientist at our company, you will be responsible for leveraging your expertise in applied data science and machine learning to drive impactful decisions through time-series forecasting and demand forecasting. Here is what you can expect in this role: Role Overview: You will be an integral part of our team, focusing on time-series forecasting, demand forecasting, and supply-chain analytics with product companies. Your primary objective will be to build forecasting models and ensure their successful implementation for real-time business decisions. Key Responsibilities: - Must have 3+ years of experience in applied data science / ML engineering, with at least 2+ years focused on time-series forecasting, demand forecasting, or supply-chain analytics with product companies - Must have built forecasting models that actually went live and were used by the business for real decisions - Must be hands-on with standard forecasting methods such as Holt-Winters, ARIMA, and Croston for intermittent/lumpy demand. You should know how to test forecasts correctly over time and which accuracy metrics to use (WAPE/MASE, not MAPE) - Must have strong day-to-day Python skills with pandas, numpy, scipy, and matplotlib, comfortable writing both quick analysis and clean, reusable code - Must possess the ability to check the data properly before modeling - looks for data leakage, trends, and seasonality - We are looking for a hands-on practitioner who works with messy real-world data, NOT a research/academic profile focused on advanced deep learning, and NOT a pure infrastructure/MLOps engineer who doesn't build models - Product companies (B2B SaaS preferred) - B.Tech/B.E from Tier 1 institutes (IITs, BITS Pilani) Qualifications Required: - Python fluency with proficiency in pandas, numpy, scipy, and matplotlib - Hands-on experience with time series forecasting methods like ETS / Holt-Winters, ARIMA, Croston, etc. - Strong statistical intuition with the ability to identify data leakage and check for stationarity, seasonality, and structural breaks - Familiarity with inventory or supply-chain math concepts such as safety stock, reorder point, EOQ, service level / fill rate, etc. - Comfort with Monte Carlo simulation and EDA discipline for thorough data analysis This role requires a candidate with a solid technical background and a proven track record in the field of data science and machine learning. If you are passionate about leveraging data to drive business decisions and have the required skills and experience, we would love to have you on board. As a Data Scientist at our company, you will be responsible for leveraging your expertise in applied data science and machine learning to drive impactful decisions through time-series forecasting and demand forecasting. Here is what you can expect in this role: Role Overview: You will be an integral part of our team, focusing on time-series forecasting, demand forecasting, and supply-chain analytics with product companies. Your primary objective will be to build forecasting models and ensure their successful implementation for real-time business decisions. Key Responsibilities: - Must have 3+ years of experience in applied data science / ML engineering, with at least 2+ years focused on time-series forecasting, demand forecasting, or supply-chain analytics with product companies - Must have built forecasting models that actually went live and were used by the business for real decisions - Must be hands-on with standard forecasting methods such as Holt-Winters, ARIMA, and Croston for intermittent/lumpy demand. You should know how to test forecasts correctly over time and which accuracy metrics to use (WAPE/MASE, not MAPE) - Must have strong day-to-day Python skills with pandas, numpy, scipy, and matplotlib, comfortable writing both quick analysis and clean, reusable code - Must possess the ability to check the data properly before modeling - looks for data leakage, trends, and seasonality - We are looking for a hands-on practitioner who works with messy real-world data, NOT a research/academic profile focused on advanced deep learning, and NOT a pure infrastructure/MLOps engineer who doesn't build models - Product companies (B2B SaaS preferred) - B.Tech/B.E from Tier 1 institutes (IITs, BITS Pilani) Qualifications Required: - Python fluency with proficiency in pandas, numpy, scipy, and matplotlib - Hands-on experience with time series forecasting methods like ETS / Holt-Winters, ARIMA, Croston, etc. - Strong statistical intuition with the ability to identify data leakage and check for stationarity, seasonality, and structural breaks - Familiarity with inventory or supply-chain math concepts such as safety stock, reorder point, EOQ, service level / fill rate, etc. - Comfort with Monte Carlo simulation and EDA discipline for thorough data analysis This role requires a candidate with a solid technical background and a proven track record in the field