Ai ML Engineer

Celebal Technologies; Mumbai; On-site; INR 1,500,000 - 2,800,000; Full time

Job summary

Celebal Technologies in Mumbai is seeking an AI/ML Engineer with 3–8 years of experience to translate business problems into analytical strategies, develop predictive and prescriptive models, and collaborate with multiple teams to drive analytics-led decisions. The role emphasizes data-driven insights, scalable assets, and adoption across divisions.

The candidate should possess strong SQL, Python/R, statistics, and BI skills, with experience in Tableau/Power BI and knowledge of ML/AI techniques,

Qualifications

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

Responsibilities

• Translate ambiguous business problems into structured analytical approaches.
• Conduct exploratory analysis, driver analyses, funnel analyses, segmentation, forecasting, and hypothesis testing.
• Build predictive, prescriptive and causal models (e.g., churn, CLTV, attribution, price elasticity).
• Develop measurement frameworks, scorecards, and reusable decision-support tools.
• Collaborate with Product, Tech, Category, Supply Chain, Marketing and Finance teams to define analytics-driven interventions.
• Work with Data Engineering on data readiness and pipeline requirements.
• Apply advanced ML/AI methods and support controlled experiments (A/B testing).

Skills

SQL

Python/R

Statistics

BI Visualization

Tools

Tableau

Power BI

Job description

Designation - AI - ML Engineer

Experience - 3-8Years

Location- Mumbai (Client location)

Job Description:

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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:

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

Good to Have

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