Data Scientist

Crunch Numbers. Build Models. Shape Strategy. Create Impact!

About LJI

Loyalty Juggernaut (LJI) isa leading B2B SaaS company redefining how enterprises drive customer engagement and loyalty. Our flagship platform, GRAVTY®, enables global brands to turn loyalty programs into measurable, revenue-generating growth engines.

Built as an AI-first, next-generation solution, GRAVTY® helps organizations deliver personalized, real-time experiences at scale, increasing customer lifetime value and deepeningbrand relationships. Headquartered in Palo Alto, California, LJI partners with enterprises across 16 major industries, including airlines, retail, hospitality, financial services and telecommunications.

Our global impact:

• 400+ Million members connected through our platform.
• 100+ Global Brands trust us to drive loyalty and brand devotion.
• 3-Time Winner of Best Technology Innovation in Loyalty.
• Global recognitions for Excellence in Loyalty Management under numerous categories.
• Recognised as a Strong performer in The Forrester Wave™ Loyalty Platforms, Q4 2025.

Explore more at www.lji.io

Role Overview:

We are seekinga Data Scientist who can turn statistical ideas into models that loyalty clients rely on every day. You will build predictive and causal models on member-level data from programs with millions of members, run them reliably in production, and explain their impact to client stakeholders.

The role is roughly 70% technical (modeling, statistics, engineering of ML systems) and 30% business (framing problems, working with clients, translating results into decisions). You will work closelywith Product, Data Engineering, Engineering and Customer Success.

What you will OWN:

• Predictive & Statistical Modeling (technical) Design, train andvalidate models for churn, propensity, next-best-offer, lifetime value, tier migration andanomaly orfraud detection. Choose methods onevidence: regression and GLMs, survival analysis, gradient boosting, probabilistic and Bayesian models, time-series forecasting, anddeep learning where it earns its complexity.
• Experimentation & Causal Inference (technical) Design A/B tests and holdouts for campaigns, offers and program changes, including power analysis and sample sizing. Apply uplift modelingand causal methods (diff-in-diff, propensity matching) to measure the true incremental impact of loyaltyinitiatives when randomization is not possible.
• Feature Engineering & Data Pipelines (technical) Build and maintainreusable feature sets from large transactional and behavioral datasets. Work with Data Engineering on data quality,lineage and scalable processing in the data warehouse and cloud platform.
• Client Engagement & Insight Delivery (business) Partner with client stakeholders to frame businessquestions as modelingproblems, define successmetrics, and present results and recommendations in executive reviews. Translate model outputs into clear actions for marketing,product and finance teams, and document models, assumptions andlimitations.

To be successfiul in this role, you should have:

1. Bachelor's or Master's degreein Statistics, Mathematics, Computer Science, Engineering, Data Science or a related quantitative field. An MBA or a postgraduate degree in Analytics is an added advantage.
2. 1–4 years of experiencebuilding and deploying ML or statistical models in production, ideally in a SaaS, consumer or loyalty/CRM setting.
3. Strong Python(pandas, NumPy, scikit-learn, XGBoost/LightGBM, statsmodels; PyTorch or TensorFlow is a plus)and strong SQL.
4. Solid statistical foundations: hypothesis testing,regression, probability, Bayesian methods, time-series, survival analysis and experimental design.
5. Experiencewith cloud data platforms such as Snowflake, BigQuery, Redshift, Databricks or Vertica, and with large-scaledata processing (Spark is a plus).
6. Working knowledge of a BI tool (Tableau, Power BI, Looker or similar) to share model outputs.
7. Experience with causal inference or uplift modeling is preferred.
8. Exposure to enterprise SaaS products or customer loyaltyprograms is preferred.
9. Strong communicationskills: able to explain model logic, trade-offs and uncertainty to non-technical stakeholders.
10. UnderstandingofAgile methodologies and comfort working in a fast-paced environment.

Key Competencies:

1. Statistical Modeling & Machine Learning
2. Experimentation & Causal Thinking
3. Production-Grade Engineering & MLOps
4. Problem Solving & Root Cause Analysis
5. Business Problem Framing
6. Stakeholder Communication & Data Storytelling
7. Attention to Detail & Reproducibility
8. Collaboration & Ownership

Why join us

1. Build ML that runs on loyaltyprograms serving 400+ million members
2. Own models end to end, from research through production
3. Work on real modeling problems: churn, uplift, forecasting and program economics
4. Collaborative, innovative environment with direct exposure to global brands
5. Strong growth and learning opportunities within a rapidly expanding organization