Job Summary

We are seeking a highly analytical and innovative Data Scientist to develop data-driven solutions that solve complex business problems. The ideal candidate will have expertise in statistics, machine learning, predictive modeling, and data analysis, with the ability to transform large, complex datasets into actionable insights. You will partner with product managers, data engineers, software engineers, and business stakeholders to build predictive models, AI-driven solutions, and advanced analytics capabilities.

Key Responsibilities

Collect, clean, and analyze structured and unstructured datasets from multiple sources.
Develop and deploy machine learning models for prediction, classification, recommendation, and forecasting.
Perform exploratory data analysis (EDA) to identify trends, patterns, and business opportunities.
Build statistical models and validate model performance using appropriate evaluation metrics.
Design and execute A/B tests and experiments to measure product or business impact.
Collaborate with Data Engineers to develop scalable data pipelines and feature engineering workflows.
Work closely with product, engineering, and business teams to translate business requirements into analytical solutions.
Communicate findings and recommendations through reports, dashboards, and presentations.
Monitor model performance and retrain models to maintain accuracy and reliability.
Stay current with advancements in AI, machine learning, and data science methodologies.

Required Qualifications

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
3–7 years of professional experience in Data Science, Machine Learning, or Advanced Analytics.
Strong programming skills in Python or R.
Advanced proficiency in SQL.
Strong understanding of probability, statistics, linear algebra, and machine learning algorithms.
Experience with supervised and unsupervised learning techniques.
Experience working with large datasets and data preprocessing techniques.
Knowledge of feature engineering, model selection, and hyperparameter tuning.
Experience deploying machine learning models into production environments.
Familiarity with Git and Agile software development practices