Data Scientist

Celebal Technologies; Hyderabad; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Celebal Technologies invites a Data Scientist to Hyderabad with 3-8 years of experience to translate business problems into analytics, build models, and deliver measurable insights. The role collaborates across Product, Tech, Marketing, and Finance, driving adoption of analytical frameworks and self-serve solutions.

Candidates should be proficient in SQL and Python/R, and capable of building actionable dashboards with BI tools. A strong foundation in ML and data storytelling is expected.

Qualifications

• Strong SQL, Python/R, and statistical modelling skills.
• Ability to translate business context into analytical problems.
• Expertise relevant to analytics tracks (Customer Intelligence, Marketing Sciences, Pricing, etc.).

Responsibilities

• Translate ambiguous business problems into structured analytical approaches.
• Conduct exploratory analysis, driver analyses, funnel analyses, segmentation, forecasting, and hypothesis testing.
• Build clear, actionable insights that drive revenue, reduce cost, or improve KPIs.
• Build predictive, prescriptive and causal models and develop scorecards and decision-support tools.
• Create modular, scalable assets for multiple divisions with standardized logic and measurement.
• Collaborate with Product, Tech, Category, Supply Chain, Marketing and Finance; work with Data Engineering on data readiness.
• Apply advanced ML/AI methods (embeddings, causal inference, MMM, uplift modelling) and support experiments.

Skills

SQL

Python/R

Statistical modelling

Tools

Tableau

PowerBI

Job description

Designation Data Science

Experience 3-8Years

Location- Hyderabad

Job Description

• Problem Solving & Insights a. Translate ambiguous business problems into structured analytical approaches. b. Conduct exploratory analysis, driver analyses, funnel analyses, segmentation, forecasting, and hypothesis testing. c. Build clear, actionable insights that drive revenue, reduce cost, or improve customer and operational KPIs.
• Model Development & Decision Frameworks a. Build predictive, prescriptive and causal models (e.g., churn, CLTV, attribution, price elasticity, anomaly detection, uplift modelling). b. Develop measurement frameworks, scorecards, and reusable decision-support tools for divisions.
• Reusable Asset & Framework Creation a. Build modular, scalable assets that can be adopted by multiple divisions with minimal customization. b. Standardize logic, definitions, taxonomies and measurement approaches within the expertise area.
• Deployment & Adoption a. Work with divisional analytics teams (hub & spoke model) to ensure adoption of frameworks, insights and models. b. Support one division deeply while enabling self-serve adoption for others, as per CoE operating model.
• Collaboration a. Partner with Product, Tech, Category, Supply Chain, Marketing and Finance teams to understand pain points and define analytics-driven interventions. b. Work closely with Data Engineering teams on data readiness and pipeline requirements.
• Innovation & Experiments a. Apply advanced ML/AI methods such as embeddings, causal inference, MMM, recommendation insights, anomaly detection, driver trees. b. Support controlled experiments (A/B testing) and design best practice experiment frameworks.

Must-Have

• Strong SQL, Python/R, and statistical modelling skills.
• Ability to translate business context into analytical problems.
• Expertise relevant to the specific track (e.g., Customer Intelligence, Marketing Sciences, Pricing, Product Analytics, etc.).
• Knowledge of ML/AI techniques: regression, classification, segmentation, causal inference, uplift modelling, MMM, forecasting, NLP, embeddings.
• Experience with BI/visualization tools (Tableau, PowerBI) for insights and storytelling.
• Understanding large-scale data environments, data hygiene, and measurement design.

Good to Have

• Structured problem solving and first principles thinking.
• Strong ownership, bias for impact, and clarity in communication.
• Ability to work in a lean, fast-paced, ambiguous setup.
• High stakeholder empathy; ability to navigate cross-functional teams.
• Curiosity, continuous learning, and the drive for excellence.