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Data Scientist (ML & Statistics)

lji

We are seeking a Data Scientist who can turn statistical ideas into models that loyaltyclients rely on every day. You will build predictive and causal models on member-leveldata from programs with millions of members, run them reliably in production, and explaintheir impact to client stakeholders.

About lji

  • LJI Loyalty Juggernaut (LJI) is a leading B2B SaaS company redefining how enterprises drivecustomer engagement and loyalty.
  • Our flagship platform, GRAVTY®, enables globalbrands to turn loyalty programs into measurable, revenue-generating growth engines.
  • Built as an AI-first, next-generation solution, GRAVTY® helps organizations deliverpersonalized, real-time experiences at scale, increasing customer lifetime value anddeepening brand relationships.
  • Headquartered in Palo Alto, California, LJI partners withenterprises across 16 major industries, including airlines, retail, hospitality, financialservices and telecommunications.
  • 400+ Million members connected through our platform.
  • 100+ Global Brands trust us to drive loyalty and brand devotion.
  • Recognized as a ‘Strong performer’ in The Forrester Wave™ Loyalty Platforms, Q4 2025.
  • Explore more about us at www.lji.io .

Responsibilities

  • Crunch Numbers.
  • Build Models.
  • Shape Strategy.
  • Create Impact!
  • Global recognitions for Excellence in Loyalty Management under numerous categories.
  • Predictive & Statistical Modeling (technical): Design, train and validate models for churn,propensity, next-best-offer, lifetime value, tier migration and anomaly or fraud detection.Choose methods on evidence: regression and GLMs, survival analysis, gradient boosting,probabilistic and Bayesian models, time-series forecasting, and deep learning where itearns its complexity.
  • Design A/B tests and holdouts forcampaigns, offers and program changes, including power analysis and sample sizing.Apply uplift modeling and causal methods (diff-in-diff, propensity matching) to measurethe true incremental impact of loyalty initiatives when randomization is not possible.
  • Build and maintain reusable featuresets from large transactional and behavioral datasets.
  • Work with Data Engineering ondata quality, lineage and scalable processing in the data warehouse and cloud platform.
  • Partner with client stakeholders toframe business questions as modeling problems, define success metrics, and presentresults and recommendations in executive reviews.
  • Translate model outputs into clearactions for marketing, product and finance teams, and document models, assumptionsand limitations.
  • ML that runs on loyalty programs serving 400+ million members Own models end to end, from research through production Work on real modeling problems: churn, uplift, forecasting and program economics Collaborative, innovative environment with direct exposure to global brands Strong growth and learning opportunities within a rapidly expanding organization

Requirements

  • To be successful in this role, you should have: Bachelor's or Master's degree in Statistics, Mathematics, Computer Science,Engineering, Data Science or a related quantitative field.
  • An MBA or a postgraduatedegree in Analytics is an added advantage.
  • 1–4 years of experience building and deploying ML or statistical models in production,ideally in a SaaS, consumer or loyalty/CRM setting.
  • Python (pandas, NumPy, scikit-learn, XGBoost/LightGBM, statsmodels; PyTorchor TensorFlow is a plus) and strong SQL.
  • hypothesis testing, regression, probability, Bayesianmethods, time-series, survival analysis and experimental design.
  • Experience with cloud data platforms such as Snowflake, BigQuery, Redshift,Databricks or Vertica, and with large-scale data processing (Spark is a plus).
  • Working knowledge of a BI tool (Tableau, Power BI, Looker or similar) to share modeloutputs.
  • able to explain model logic, trade-offs and uncertainty tonon-technical stakeholders.
  • Understanding of Agile methodologies and comfort working in a fast-pacedenvironment.
  • Key Competencies Statistical Modeling & Machine Learning Experimentation & Causal Thinking Production-Grade Engineering & MLOps Problem Solving & Root Cause Analysis Business Problem Framing Stakeholder Communication & Data Storytelling Attention to Detail & Reproducibility Collaboration & Ownership Why join us?

Preferred

  • Experience with causal inference or uplift modeling is preferred.
  • Exposure to enterprise SaaS products or customer loyalty programs is preferred.

Benefits

  • 3-Time Winner of “Best Technology Innovation in Loyalty”.

Role Overview

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 closely with Product, Data Engineering, Engineering and Customer Success.