Junior Data Scientist

Job Title: Junior Data Scientist / Forecasting Analyst

Location: Offshore – India

Engagement Type: Contract (4 Months, Extendable)

Start Date: 1st July 2026

Experience Required: 3–4 Years Overall Experience

Relevant Experience: Minimum 1.5+ Years in Data Science, Machine Learning, or Predictive Analytics

About the Role

We are seeking a motivated and detail-oriented Junior Data Scientist / Forecasting Analyst to support our Data Science team in the development, execution, and maintenance of predictive analytics solutions focused on demand forecasting. The ideal candidate will work closely with senior data scientists and business stakeholders, contributing to data preparation, model execution, forecasting operations, and continuous process improvement.

This role provides an excellent opportunity to gain hands-on experience in forecasting methodologies, machine learning workflows, and large-scale predictive analytics projects within a collaborative international environment.

Key Responsibilities

Predictive Analytics & Forecasting Support

• Support the implementation, execution, and maintenance of demand forecasting and predictive analytics solutions.

• Assist in analyzing historical sales data, demand patterns, and business variables to generate actionable insights.

• Execute existing forecasting models under the guidance of senior team members.

• Perform model retraining activities and generate periodic forecasts according to established schedules.

• Monitor model outputs and ensure the timely delivery of forecasting results.

Data Preparation & Feature Engineering

• Prepare, clean, and validate datasets used for predictive modeling.

• Perform data quality assessments and identify anomalies, inconsistencies, and missing data.

• Support feature engineering activities following established team methodologies and best practices.

• Collaborate with senior data scientists to improve data readiness and model inputs.

Model Monitoring & Performance Evaluation

• Calculate and report forecast accuracy metrics and model performance indicators.

• Monitor forecasting outputs and identify deviations, anomalies, or unexpected trends.

• Document findings and escalate issues when necessary.

• Support continuous improvement efforts to enhance model reliability and forecasting accuracy.

Process Automation & Technical Support

• Assist in automating data preparation, model execution, and prediction generation processes.

• Develop and maintain Python scripts and simple data pipelines.

• Support data workflow optimization initiatives aimed at improving efficiency and scalability.

• Participate in troubleshooting and resolving technical issues related to forecasting processes.

Reporting & Documentation

• Prepare reports, dashboards, tables, visualizations, and result summaries for internal stakeholders.

• Maintain accurate technical documentation for models, datasets, processes, and workflows.

• Ensure all deliverables meet organizational quality standards and documentation requirements.

• Contribute to knowledge sharing and process documentation initiatives.

Team Collaboration

• Participate actively in daily stand-ups, sprint reviews, and weekly technical meetings.

• Provide regular updates on work progress, risks, and blockers to the onshore team.

• Collaborate effectively within a distributed global team environment.

• Proactively seek clarification when requirements are unclear and escalate concerns appropriately.

Required Qualifications

Education

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative discipline.

Experience

• 3–4 years of overall professional experience.

• Minimum 1.5+ years of hands-on experience in Data Science, Machine Learning, Predictive Analytics, or Forecasting-related projects.

• Experience working with structured datasets and analytical workflows.

Mandatory Technical Skills

Programming & Data Analysis

• Strong proficiency in Python

• Pandas

• NumPy

• Scikit-learn

• Experience with Git version control.

• Strong understanding of SQL fundamentals for data extraction and manipulation.

Machine Learning & Statistics

• Understanding of machine learning fundamentals.

• Knowledge of:

• Regression techniques

• Model validation methodologies

• Forecast accuracy measurements

• Statistical analysis concepts

• Error metrics (MAE, RMSE, MAPE, etc.)

Data Management

• Data cleaning and preprocessing.

• Data quality validation and anomaly detection.

• Exploratory Data Analysis (EDA).

Preferred / Nice-to-Have Skills

• Demand Forecasting methodologies.

• Time Series Analysis and Forecasting (academic or professional experience).

• PySpark.

• Azure Databricks.

• Data Visualization tools and libraries:

• Power BI

• Tableau

• Matplotlib

• Seaborn

• Experience working with cloud-based analytics platforms.

• Basic understanding of MLOps concepts and model lifecycle management.

Core Competencies

• Strong attention to detail and commitment to data quality.

• Excellent analytical and problem-solving skills.

• Effective written and verbal communication.

• Ability to work in a distributed and multicultural team environment.

• Strong learning mindset and curiosity about data science methodologies.

• Ability to manage multiple tasks and priorities effectively.

• Proactive attitude toward identifying risks, issues, and improvement opportunities.

• Growing ability to work independently while seeking guidance when required.

Daily Activities

• Analyze historical sales and business data to support forecasting initiatives.

• Execute forecasting models and generate scheduled prediction outputs.

• Perform quality checks on incoming data prior to forecasting cycles.

• Compute and report forecast performance metrics.

• Investigate and document forecasting deviations and anomalies.

• Maintain and enhance Python scripts used in forecasting workflows.

• Support automation of recurring analytical tasks and processes.

• Prepare charts, tables, and summaries for business and technical reporting.

• Update project documentation and knowledge repositories.

• Participate in team meetings and provide progress updates to onshore stakeholders.