Data Scientist with Python

As a candidate for the position, you are expected to have a strong technical background with fluency in Python at a daily-driver level. You should be proficient in libraries such as pandas, numpy, scipy, and matplotlib, and be comfortable working in notebooks and writing modular code. Key Responsibilities: - Hands-on experience with time series forecasting, including ETS / Holt-Winters, ARIMA, Croston, or similar intermittent-demand methods. You should understand temporal cross-validation and be able to explain why standard k-fold breaks on time series. Additionally, you should know how to handle zero-inflated data and use metrics like WAPE and MASE instead of MAPE. - Possess statistical intuition to identify overfitting models, data leakage, and check for stationarity, seasonality, and structural breaks before model fitting. - Familiarity with inventory or supply-chain math concepts such as safety stock, reorder point, EOQ, service level / fill rate, and (s,S) policies. While deriving formulas is not required, you should be able to interpret them and understand the underlying assumptions. - Comfortable with Monte Carlo simulations, able to create simple inventory simulations using numpy and comprehend concepts like bootstrap, sampling distributions, and interpreting simulation results. - Practice disciplined exploratory data analysis (EDA) by systematically addressing key questions about the dataset, including row count, null rate, data types, distribution, time coverage, and key uniqueness. You should also create a concise summary of the dataset before proceeding with any modeling tasks. Qualifications Required: - Proficiency in Python, specifically in libraries like pandas, numpy, scipy, and matplotlib. - Experience in time series forecasting and statistical modeling. - Understanding of inventory or supply-chain math concepts. - Familiarity with Monte Carlo simulations and exploratory data analysis practices. If you are passionate about leveraging your technical skills in Python, statistical modeling, and data analysis to drive impactful decisions in inventory management or supply chain operations, this role offers an exciting opportunity for you. As a candidate for the position, you are expected to have a strong technical background with fluency in Python at a daily-driver level. You should be proficient in libraries such as pandas, numpy, scipy, and matplotlib, and be comfortable working in notebooks and writing modular code. Key Responsibilities: - Hands-on experience with time series forecasting, including ETS / Holt-Winters, ARIMA, Croston, or similar intermittent-demand methods. You should understand temporal cross-validation and be able to explain why standard k-fold breaks on time series. Additionally, you should know how to handle zero-inflated data and use metrics like WAPE and MASE instead of MAPE. - Possess statistical intuition to identify overfitting models, data leakage, and check for stationarity, seasonality, and structural breaks before model fitting. - Familiarity with inventory or supply-chain math concepts such as safety stock, reorder point, EOQ, service level / fill rate, and (s,S) policies. While deriving formulas is not required, you should be able to interpret them and understand the underlying assumptions. - Comfortable with Monte Carlo simulations, able to create simple inventory simulations using numpy and comprehend concepts like bootstrap, sampling distributions, and interpreting simulation results. - Practice disciplined exploratory data analysis (EDA) by systematically addressing key questions about the dataset, including row count, null rate, data types, distribution, time coverage, and key uniqueness. You should also create a concise summary of the dataset before proceeding with any modeling tasks. Qualifications Required: - Proficiency in Python, specifically in libraries like pandas, numpy, scipy, and matplotlib. - Experience in time series forecasting and statistical modeling. - Understanding of inventory or supply-chain math concepts. - Familiarity with Monte Carlo simulations and exploratory data analysis practices. If you are passionate about leveraging your technical skills in Python, statistical modeling, and data analysis to drive impactful decisions in inventory management or supply chain operations, this role offers an exciting opportunity for you.