Data Scientist (ML & Statistics)
Loyalty Juggernaut, Inc (company site, opens in a new tab)1–4 yrsHyderabadOn-siteDirect career pageEst. 8–16 LPA
Posted 20h ago
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Reach people at Loyalty Juggernaut, Inc
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Loyalty Juggernaut, Inc you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, train, validate, and deploy predictive and causal models on large-scale loyalty program data, including churn, propensity, lifetime value, and fraud detection.
- Lead design of experimentation frameworks (A/B tests, holdouts) and apply causal inference methods to measure the incremental impact of loyalty initiatives.
- Collaborate with clients and cross-functional teams to translate business questions into modeling problems, present insights, and enable data-driven decisions for marketing, product, and finance.
Minimum Requirements
- Bachelor's or Master's degree in Statistics, Mathematics, Computer Science, Engineering, Data Science, or related quantitative field.
- 1-4 years of experience building and deploying ML or statistical models, preferably in SaaS, consumer, or loyalty/CRM domains.
- Proficiency in Python (pandas, NumPy, scikit-learn, XGBoost/LightGBM, statsmodels) and strong SQL skills.
- Experience with cloud data platforms (Snowflake, BigQuery, Redshift, Databricks, or Vertica) and large-scale data processing; BI tools knowledge (Tableau, Power BI, Looker) also required.
Ideal Candidate Profile
- Experience in production-grade ML model development plus experimentation design and application of causal inference techniques.
- Exposure to enterprise SaaS products or customer loyalty programs, demonstrating domain familiarity.
- Ability to clearly communicate complex model logic, assumptions, and insights to non-technical stakeholders while working in fast-paced, Agile environments.