Qualifications & Experience
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
3–6 years of hands-on experience in the Data Science domain, with a strong track record of solving real-world business problems using data.
Experience working in cross-functional environments, collaborating with business stakeholders, product teams, and engineering teams.
Core Technical Skills
Strong understanding of data science fundamentals, including data structures, algorithms, statistical modeling, and database systems.
Advanced proficiency in SQL for complex querying, data manipulation, and performance optimization.
Strong programming skills in Python, including experience with libraries such as Pandas, NumPy, Scikit-learn, and other relevant frameworks.
Machine Learning & Advanced Analytics
Extensive experience in building, evaluating, and deploying machine learning models in production environments.
Expertise in feature engineering, model selection, hyperparameter tuning, and performance monitoring.
Solid understanding of supervised and unsupervised learning techniques, as well as exposure to advanced methods such as time series forecasting, NLP, or deep learning.
Strong grounding in statistical analysis, hypothesis testing, and experimental design (A/B testing) to derive actionable insights.
Data Engineering & Systems Knowledge
Working knowledge of distributed computing frameworks (e.g., Apache Spark) and large-scale data processing systems.
Experience in designing and maintaining data pipelines, ETL processes, and data transformation workflows.
Familiarity with cloud platforms such as Azure, AWS, or GCP and exposure to MLOps practices, including model deployment and monitoring.
Visualization & Communication
Proficiency in data visualization tools such as Tableau, Power BI, or Superset for developing dashboards and reports.
Ability to translate complex analytical findings into clear, concise, and actionable insights tailored to both technical and non-technical stakeholders.
Strong communication and storytelling skills to influence decision-making.
Ownership & Leadership
Ability to lead end-to-end data science projects, from problem definition and data exploration to model deployment and business adoption.
Experience mentoring junior team members and contributing to best practices, code quality, and knowledge sharing.
Strong problem-solving mindset with attention to detail and a focus on delivering business impact.