Data Sciences · Bengaluru

What will you be doing?

1. Experiment Design and Interpretation:

• Lead and design complex experiments, addressing challenges with statistical rigor.

• Interpret results and provide actionable insights to drive business decisions.

2. Data Engineering Workflow and Design:

• Partner with stakeholders to ensure data systems and structures are efficient and reliable.

• Reduce turnaround time (TAT) for analytics, feature exploration, and experimentation setup

3. Data Science Model Integration:

• Collaborate with data science teams to integrate models into workflows.

• Define success metrics, identify inefficiencies, and leverage model outputs to enhance operations.

4. Proactive and Reactive Analysis:

• Conduct exploratory data analysis, simulations, and hypothesis testing.

• Provide strategic support and actionable insights to address business challenges.

5. Health Metrics Design and Implementation:

• Define, implement, and monitor key performance metrics to assess platform performance and user behavior.

6. Leadership and Collaboration:

• Serve as a liaison between cross-functional teams, advocating for data-driven decisions.

• Mentor analysts and promote a culture of analytics within the organization.

7. Product Impact Tracking:

• Design and implement analyses to monitor product impact.

• Ensure alignment of analytics initiatives with broader business objectives.

What are we looking for?

Skills and Attributes:

• 8–15 years of total professional experience, preferably in Tech, Consulting, or high-scale Analytics organizations.

• 4+ years of people management experience, leading data science, analytics, or operational analytics teams in system-heavy environments.

• Advanced proficiency in SQL and Python — able to drive deep analyses, RCA investigations, and metric instrumentation independently.

• Hands-on experience with Spark or distributed data processing frameworks for scalable analysis.

• Proven ability to design and deploy control systems or feedback loops in collaboration with engineering teams to tune complex algorithms in production.

• Successfully implemented and maintained Data Science models in production, including monitoring and post-deployment tuning.

• Solid foundation in statistical methods, hypothesis testing, and experimental design, especially as applied to system performance and anomaly detection.

• Comfort working with observability and visualization tools — Tableau, Looker, Power BI, or internal metric dashboards like Grafana or Superset.

• Strong record of cross-functional collaboration with engineering, product, SRE, and ML teams to drive system health and platform improvements.

• Demonstrated ability to work through ambiguity, structure diagnostic approaches, and drive data-informed operational decisions.

• Strong strategic and systems thinking mindset — able to reason about interdependent metrics and optimize for system-wide health.