Time Series Data Scientist/Forecasting Analyst - Python

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.

2. 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.

3. Statistical Analysis :

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

- Conduct model diagnostics and ensure statistical validity of predictions.

4. 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.

5. 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 Masters degree in Data Science, Statistics, Computer Science, Mathematics, or related field.

- 3 to 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.

Soft Skills :

- Proactive, self-driven, and capable of taking ownership.

- Strong collaboration and interpersonal skills.

- Ability to manage multiple tasks and meet deadlines.

- Business acumen with exposure to supply chain concepts.