Position Summary
EarnIn is undergoing transformative investments in machine learning products, algorithms, and platforms. We seek highly skilled and experienced ML engineers to start and lead our algorithmic marketing initiatives from our India office in Bengaluru. You will apply machine learning models, statistical techniques, experiment design, and data analysis to optimize marketing spend on user acquisition and retention, ensuring the most efficient use of resources. You will catalyze our healthy and growing business, creating first-of-a-kind success stories for marketing through machine learning.
This position will be a hybrid role from our Bengaluru office with 2 days a week in the office.
What You'll Do
- Design, build, A/B test, and maintain machine learning and statistical models to optimize audience, channel, campaign, creative, and customer journey, improving ROI for user acquisition and lifecycle marketing.
- Continuously optimize the quality of our machine learning models for Marketing Mix Modeling (MMM), predicted Lifetime Value (LTV), incremental Lift estimation, and causal inference.
- Analyze large datasets to extract actionable insights and inform marketing decisions.
- Collaborate with multiple teams in the US and India. Communicate findings and recommendations to stakeholders clearly and concisely.
- Champion scientific rigor in data-driven decision-making.
- Stay up-to-date with the latest advancements in machine learning, statistical techniques, and marketing analytics to recommend newer business opportunities.
What We're Looking For
- Bachelor's or Master’s degree in Computer Science, Engineering, or a related field.
- 2+ years of industry machine learning experience and excellent software engineering skills.
- Familiarity with digital marketing concepts and metrics. Prior experience with Marketing Mix Modeling (MMM), predicted Lifetime Value (LTV), incremental Lift estimation, geo lift tests, and causal inference is a big plus.
- Expertise in the breadth of machine learning and statistical techniques such as tree-based models, deep learning techniques, and A/B testing.
- Strong programming skills in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, XGBoost, and Scikit-Learn.
- Hands-on experience with SQL and data manipulation tools.
- Experience with ML cloud platforms such as AWS Sagemaker, Databricks, or GCP Vertex AI.
- Strong analytical skills to evaluate business impact and performance metrics of ML implementations and drive continuous improvement.
- Strong communication and collaboration skills.
- Passion for learning and staying updated with the latest machine learning industry trends.