About the role
Primary Skills
- Data Scientist
- Translate complex business problems into structured, data- and model-driven narratives. Partner with stakeholders to frame ambiguous problem statements, perform deep exploratory Data Analysis (EDA), Visualizations, Hypothesis A/B Testing/ What-if analysis and scenario modeling, Algorithm development to identify inefficient sectors (DL/UL) by flagging outliers using performance KPIs.
- Communicate statistically sound, model-backed insights that directly influence strategic and operational decisions.
- Apply advanced statistical techniques, classical ML, and modern AI approaches to forecast outcomes and recommend next-best actions.
- Experience in driving initiatives through a rigorous lifecycle—problem formulation → hypothesis generation → EDA → feature engineering → modeling → evaluation → visualization → measurable business impact—ensuring scientific rigor, interpretability, and alignment with business objectives.
- Develop, evaluate, and iterate on analytical, machine learning, and hybrid AI models to uncover patterns, trends, and anomalies, solving complex problems such as churn prediction, revenue optimization, network efficiency, and operational optimization.
- Demonstrate strong hands-on expertise in BigQuery, SQL, and Python to build scalable data pipelines, feature stores, and analytical workflows, ensuring performance, reproducibility, and accuracy on large-scale datasets.
- Ensure consistency and reliability across diverse data sources through strong data validation, monitoring, and governance practices. Maintain trust in analytical and AI-driven outcomes through robust data quality checks, model validation, and ongoing performance monitoring.
- Collaborate with cross-functional teams (data engineering, SRE, platform, and business teams) to operationalize AI-driven insights, ensure reliability, and deliver measurable business impact.
- Drive continuous improvement initiatives by integrating feedback loops, monitoring KPIs on data reliability aligned to enterprise goals.
- Mentorship & Collaboration – Guide junior analysts, promote knowledge sharing, and foster a culture of analytical excellence across teams.
- Analytical Mindset & Self-Starter – Proactive in identifying opportunities, framing problems, and communicating insights in a business-first language, bridging the gap between data science and strategy.
- Data Visualization & Dashboards – Transform raw numbers into intuitive dashboards and visual stories that resonate with both technical and non-technical audiences, enabling faster decisions.
- Skills:
- Strong proficiency in Advanced SQL with experience in writing optimized queries for large datasets.
- Expertise in data analytics with a solid understanding of statistical methods and data interpretation.
- Exposure to Data Science concepts, including predictive modeling and machine learning techniques.
- Hands-on experience with Python, R, or similar analytical tools is a plus.
- Experience working with large-scale datasets and business intelligence tools.
- Strong problem-solving abilities with the capability to translate business problems into analytical solutions.
- Excellent communication and presentation skills to convey insights effectively to stakeholders.
- Familiarity with cloud platforms such as AWS, Azure, or GCP for data processing and analytics.
Specialization
- Data Science Advanced: Data Science Lead
Job requirements
- Data Science Advanced: Data Science Lead