Data Scientist

About the Role

We are seeking an experienced Data Scientist to bridge the gap between theoretical data science, statistical modeling, and practical software engineering. In this role, you will take full ownership of the end-to-end machine learning project lifecycle—transforming complex business challenges into predictive models, building robust data pipelines, and deploying production-ready ML systems that drive measurable business impact.

If you bring 3-5 years of hands-on data science experience and thrive on using data to automate decision-making and solve real-world operational problems, this role offers high visibility and technical ownership.

Key Responsibilities

• End-to-End ML Ownership: Drive the complete machine learning lifecycle, from problem formulation and data collection to model training, deployment, and performance monitoring.
• Predictive Analytics & Advanced Modeling: Conduct exploratory data analysis and build high-performance statistical, machine learning, and deep learning models to extract actionable insights.
• ML Architecture & Deployment: Design and construct scalable ML pipelines and infrastructure, deploying models into production with a focus on reliability, efficiency, and low latency.
• Strategic Stakeholder Alignment: Translate complex technical findings and algorithmic outputs into business recommendations for leadership and cross-functional teams.

Key Requirements & Qualifications

• Work Experience: 3-5 years of professional experience in applied data science, predictive modeling, data management, and business intelligence/visualization.

• Education: Bachelor’s Degree in Mathematics, Statistics, Applied Mathematics, Computer Science, Industrial Engineering, or a related quantitative field.

• Technical Proficiency:

  • Advanced: Machine learning algorithms (regression, classification, clustering, deep learning) and statistical techniques.
  • Advanced: Strong programming skills in Python, R, and SQL for data manipulation and model engineering.
  • Intermediate: Feature engineering, large dataset management, database querying (e.g., DBeaver), and pipeline construction.
  • Tools: Familiarity with visualization tools (Tableau) and modern data stack environments.

• Business Acumen & Soft Skills: Ability to frame vague business problems into analytical models, with solid communication and storytelling skills to engage non-technical stakeholders.