🕒 yesterday
Leads high-impact AI projects in life sciences, defining data science approaches and software architecture while mentoring colleagues, shaping client engagements, and ensuring rigorous delivery.
Senior Data Science - R01560392
📅 Jan 28
Data ScienceAdvanced SQLBigqueryPython
📜 Description
- Translate ambiguous business problems into analytical questions, hypotheses, and model-driven narratives.
- Perform exploratory analysis, visualization, A/B testing, scenario modeling, and outlier detection using performance KPIs.
- Develop, evaluate, and iterate on statistical, machine learning, and hybrid AI models for forecasting, churn, revenue, network, and operational optimization.
- Build scalable data pipelines, feature stores, and analytical workflows using BigQuery, SQL, and Python.
- Maintain data quality and model reliability through validation, governance, monitoring, and feedback loops.
- Partner with data engineering, SRE, platform, and business teams; communicate insights and mentor junior analysts.
🛠️ Requirements
- Strong proficiency in advanced SQL, including optimized queries for large datasets.
- Expertise in data analytics, statistical methods, and data interpretation.
- Knowledge of predictive modeling and machine learning techniques.
- Hands-on experience with Python, R, or similar analytical tools is a plus.
- Experience with large-scale datasets and business intelligence tools.
- Strong problem-solving, stakeholder communication, and presentation skills.
- Familiarity with AWS, Azure, or GCP for data processing and analytics.
Full job description
Senior Data Science Lead
Primary Skills
• 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.
• 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.
Specialization
Job requirements
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