Time Series Data Scientist

Overview

Role Overview

We are seeking a skilled and proactive Time Series Data Scientist / Forecasting Analyst to design, develop, and optimize forecasting models for business-critical applications. The ideal candidate will have strong expertise in time series modeling, data manipulation, and machine learning techniques, along with excellent communication and problem-solving skills. Experience in supply chain, demand planning, or large-scale time series datasets is a plus.

Key Responsibilities

1. Time Series Modeling & Machine Learning

Develop, validate, and deploy forecasting models using techniques such as ARIMA, SARIMA, Prophet, LSTM, RNNs, and other ML algorithms.

Build scalable and automated forecasting pipelines to support business decision-making.

Optimize model performance through hyperparameter tuning and continuous monitoring.

1. Data Analysis & Feature Engineering

Perform data cleaning, manipulation, transformation, and exploratory analysis using Python (pandas, numpy), SQL, or BI tools.

Create and select meaningful features to enhance model accuracy and interpretability.

Manage and process large-scale time series datasets efficiently.

1. Statistical Analysis

Apply statistical methods such as hypothesis testing, regression analysis, and variance analysis.

Conduct model diagnostics and ensure statistical validity of predictions.

1. Business Collaboration

Work closely with D&T teams, cross-functional stakeholders, and business units to translate requirements into analytical solutions.

Present insights, findings, and model outcomes in clear and business-friendly language.

1. Project Ownership

Manage project timelines, deliverables, and documentation with minimal supervision.

Proactively identify opportunities for process improvement, automation, and efficiency.

Required Skills & Qualifications

Bachelor's or Master’s degree in Data Science, Statistics, Computer Science, Mathematics, or related field.

3–7 years experience in data science, forecasting, or machine learning roles.

Strong proficiency in Python and libraries such as pandas, numpy, scikit-learn, matplotlib, seaborn.

Hands-on experience with time series forecasting models and ML modeling.

Strong statistical knowledge and ability to apply statistical methods to real-world problems.

Experience handling large-scale time series data and optimizing data pipelines.

Excellent communication, presentation, and stakeholder management skills.

Strong analytical and problem-solving abilities.

Preferred Qualifications

Experience in supply chain, demand planning, inventory forecasting, or related domains.

Exposure to AWS, Azure, GCP, or MLOps tools.

Experience with BI tools like Tableau, Power BI, or Looker.

Knowledge of Deep Learning frameworks such as TensorFlow or PyTorch.