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SourcingXPress Linkedin · Posted 4mo ago

Data Scientist

India

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Indexed description

Company: OnMobile

Website: Visit Website

LinkedIn: Visit LinkedIn

Business Type: Enterprise

Company Type: Product

Business Model: B2B

Funding Stage: IPO/Public

Industry: Telecom / Digital Entertainment

Salary Range: ₹ 25-50 Lacs PA

Job Description

OnMobile Global, headquartered in Bangalore and present in 65+ countries, is a listed company and a leader in mobile gaming and entertainment. With a proven track record of defining what digital engagement looks like, we transformed into a mobile gaming-first company with platforms like Challenges Arena and ONMO, while also building enterprise solutions such as Gamize and Buzzmo. We pair cutting-edge tech with a culture that’s collaborative, high-energy and fun. If you’re passionate about building the future of digital experiences, we’d love to have you take the next step in your journey here.

Role Overview

The Data Scientist is a core member of the AI & Data Science CoE, responsible for building predictive and prescriptive analytics models that directly drive business outcomes across OnMobile’s product portfolio.

This role works closely with POD-embedded data analysts, ML engineers, and business stakeholders to translate complex business problems into analytical frameworks, design experiments, and deliver actionable insights. With access to 15+ TB of user behaviour, transaction, and performance data on BigQuery, this role is pivotal to OnMobile’s transformation to a data-driven decision model.

Roles & Responsibilities

Predictive & Prescriptive Analytics

  • Design, build, and validate predictive models for churn, LTV, revenue forecasting, and user behavior across Tones and Gaming products.
  • Develop prescriptive analytics solutions for campaign budget optimization, dynamic pricing, and content recommendation.
  • Build decay models for revenue, subscriber base, and ARPU at operator and portfolio levels.

Experimentation & Statistical Analysis

  • Apply rigorous statistical methods (hypothesis testing, causal inference, Bayesian analysis) to drive evidence-based decision-making.
  • Identify high-LTV and high-ARPU parameter combinations and develop replication strategies across operators.

Insight Generation & Stakeholder Collaboration

  • Work with pod-embedded data analysts to translate complex model outputs into actionable business recommendations.
  • Present findings to pod leaders and senior leadership using clear, narrative-driven reporting.
  • Collaborate with ML engineers to productionize models and ensure they meet performance and reliability SLAs.
  • Partner with AI/GenAI engineers to integrate model outputs into automated insight generation pipelines.

Data Exploration & Feature Engineering

  • Explore and analyse the 15+ TB data estate on BigQuery to discover patterns, anomalies, and opportunities.
  • Design and build feature engineering pipelines that feed into ML models and the feature store.
  • Contribute to data quality improvement and data governance practices within the CoE.

Must-Have Technical Skills

  • Strong proficiency in Python (pandas, scikit-learn, statsmodels, XGBoost, LightGBM) and SQL (BigQuery).
  • Deep understanding of statistical modeling, time-series analysis, survival analysis, and regression techniques.
  • Experience with classification, clustering, recommendation systems, and optimization algorithms.
  • Familiarity with deep learning frameworks (TensorFlow/PyTorch) for NLP or structured data problems.
  • Experience with experiment design (A/B testing, multi-armed bandits) and causal inference methods.
  • Working knowledge of data visualization tools (Looker, Tableau, or Metabase).

Domain & Business Skills

  • Experience in telecom, subscription services, digital marketing, or gaming analytics is strongly preferred.
  • Understanding of unit economics: CAC, LTV, ARPU, churn, retention cohorts.
  • Ability to translate business problems into well-defined analytical frameworks.
  • Strong communication skills to present complex analyses to non-technical stakeholders.

Collaboration & Process

  • Experience working with cross-functional teams (product, marketing, operations, engineering).
  • Familiarity with ML lifecycle: model development, validation, deployment handoff, monitoring.
  • Experience with version control (Git), experiment tracking (MLflow/Weights & Biases), and notebook environments.

Good To Have Skills

  • Experience with GCP ecosystem (BigQuery, Vertex AI, Dataform, Cloud Composer).
  • Exposure to LLM-based analytics or GenAI applications (RAG, prompt engineering).

Benefits & Perks

  • Flexible Work Hours
  • Industry best Insurance Coverage
  • Leave Entitlements
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