Data Scientist

About The Opportunity

• Design, develop, and deploy robust machine learning models for classification, regression, and recommendation tasks using Python and R.
• Build and maintain scalable data pipelines for ingestion, transformation, and feature engineering across structured and unstructured sources.
• Perform statistical analysis and A/B testing to validate model performance, ensuring reliability and accuracy in production environments.
• Create interactive dashboards and visualizations to communicate insights and KPIs to stakeholders using Tableau, Power BI, or equivalent tools.
• Collaborate with cross-functional teams—including product managers, engineers, and business stakeholders—to define requirements and translate them into analytic solutions.
• Monitor model drift, automate retraining workflows, and implement continuous improvement processes to maintain high performance.
  

Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
• 2–5 years of hands-on experience in data science, with strong proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL.
• Solid understanding of statistical modeling, hypothesis testing, and experimental design.
• Experience building and deploying ML models in production environments (e.g., Docker, Airflow, MLflow).
• Ability to translate complex data into clear, actionable recommendations and visualizations.
• Excellent problem-solving skills, with a data-driven mindset and attention to detail.
  

Preferred

• Experience with big data technologies such as Spark, Hadoop, or Hive.
• Familiarity with cloud platforms (AWS, Azure, or GCP) and their ML/analytics services.
• Knowledge of deep learning frameworks (TensorFlow, PyTorch) for advanced modelling.
• Exposure to NoSQL databases (MongoDB, Cassandra) and real-time data processing.
  

• Competitive compensation with performance-based incentives and annual learning stipends.
• Vibrant, collaborative on-site environment fostering continuous learning and innovation.
• Opportunities for career growth, mentorship programs, and cross-functional project ownership.