Posted:7 hours ago|
Platform:
Remote
Full Time
Job ID: 272985
About GreedyGame
GreedyGame is a leading ad-tech company that's been driving app growth and monetization.
We’ve built a sustainable and profitable business for over 7 years without chasing vanity metrics and successfully completed our 0 1 journey, we’re now scaling rapidly (1We are looking for a Data Scientist who can turn this data into foresight building models that predict campaign outcomes, forecast publisher revenue, and power decision-making at scale.
Responsibilities-
Revenue Forecasting: Build predictive models to estimate publisher revenue streams, fill rates, and eCPM across different ad networks and geographies.
User Behavior Prediction: Analyze in-app user journeys and predict churn, retention, and engagement to help publishers personalize experiences.
Ad Performance Modeling: Develop CTR and conversion rate prediction models to optimize bidding strategies and ad placements.
Inventory Forecasting: Forecast ad inventory availability and demand to help sales and publisher teams plan better.
Campaign Optimization: Work with account managers and product teams to provide real-time insights on campaign pacing, under-delivery risks, and performance anomalies.
Experimentation: Design and run experiments (A/B/n tests) to evaluate new ad formats, placements, and targeting algorithms.
Data Products: Partner with engineering to embed forecasting models into GreedyGame’s platform, enabling self-serve insights for publishers and advertisers.
Requirements-
2–4 years of hands-on experience in predictive analytics, time series forecasting, or applied ML.
Strong proficiency in Python (Pandas, NumPy, Scikit-learn, Statsmodels) and SQL.
Familiarity with MLOps practices (model deployment, monitoring, retraining).
Solid understanding of statistical inference, experiment design, and causal analysis.
Knowledge of real-time prediction pipelines (Kafka, Spark)
Nice to Have
Exposure to adtech, gaming analytics, or digital marketing intelligence.
Familiarity with retrieval-augmented generation (RAG) or fine-tuning for domain-specific insights.
Experience with cloud AI/ML services (GCP Vertex AI, AWS Sagemaker, Azure ML).
Exposure to GenAI and LLMs for applied use cases (e.g., automated report generation, text summarization of campaign insights, anomaly explanation, or predictive storytelling).
Why GreedyGame?
Workplace Type
Employment Type
Experience Level
Work Experience (years)
Skills
Sql
Mlops
Data
A/B Testing
Genai
Forecasting
Python
Prediction
GreedyGame Media
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