Position listed on behalf of a partner company managing all applications and next steps.
Partner is looking for a Senior Applied Machine Learning Engineer based in India (Remote).
Responsibilities
Production Model Optimization: Improve existing machine learning models for churn prediction, customer retention, account health, and segmentation using structured customer data and product usage signals.
Prediction Quality and Evaluation: Measure model performance against actual renewal, churn, expansion, and adoption outcomes.
Statistical Analysis and Backtesting: Develop time-based backtesting methodologies and fixed test sets to evaluate models rigorously and prevent data leakage.
Prediction Error Investigation: Analyze incorrect predictions at the individual customer account level to identify problems with labels, features, source data, or underlying assumptions.
Data Quality and Feature Engineering: Investigate inconsistent CRM records and product usage data, address labeling issues, identify data leakage, and improve feature quality.
Production Implementation: Translate analytical findings and model improvements into production-ready changes within the existing Go-based product codebase.
Model Monitoring and Automation: Automate evaluation processes and implement monitoring mechanisms that detect declines in recall, label quality, and product usage signals.
Initial Model Assessment: Conduct recall analysis for every existing customer using fixed test sets and implement improvements.
New Predictive Capabilities: Build, test, and validate additional models for expansion opportunities, cross-selling, forecasting, and marketing applications.
Business-Focused Communication: Explain model performance, prediction errors, statistical findings, and recommended improvements in clear, accessible language.
Data Confidentiality: Protect sensitive customer information, internal queries, training datasets, and analytical results.
Cross-Functional Collaboration: Work closely with backend engineering colleagues.
Independent Problem-Solving: Take ambiguous machine learning challenges from initial investigation through hypothesis development, testing, implementation, and production validation.
Continuous Improvement: Establish repeatable evaluation practices and continuously refine models, datasets, and monitoring systems.
Requirements
Professional Experience: At least five years of applied machine learning experience, specifically working with structured business data and production systems.
Academic Foundation: Strong mathematical and statistical fundamentals, ideally supported by a rigorous academic background. A degree from an IIT or a similarly strong academic program is preferred.
Production ML Ownership: Demonstrated experience developing, deploying, and maintaining production models that directly inform business decisions.
Machine Learning Fundamentals: Strong understanding of classification methods, precision, recall, model evaluation, fixed test sets, and time-based validation techniques.
Data Quality and Leakage Prevention: Proven ability to identify and resolve data leakage, incorrect labels, weak features, and inconsistencies.
Business Data Experience: Practical experience working with complex, imperfect datasets from CRM systems and product usage platforms.
SQL Proficiency: Strong SQL skills, including the ability to query, analyze, and validate large datasets.
Software Engineering Skills: Ability to work with Go application code and integrate machine learning improvements into an existing production codebase. Python may be used for analysis.
Model Evaluation and Error Analysis: Experience investigating individual predictions, identifying root causes of errors.