Senior Applied Machine Learning Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Applied Machine Learning Engineer based in India (Remote).

As a Senior Applied Machine Learning Engineer, you will take ownership of production machine learning models that help businesses make smarter, data-driven customer decisions. You will improve churn prediction, customer retention, account health, and segmentation models using real-world business and product usage data. Your work will focus on making predictions more accurate, explainable, and reliable by identifying data quality issues, evaluating model performance, and investigating errors. Working at the intersection of machine learning, statistics, and product engineering, you will turn analytical insights into production-ready improvements. You will collaborate closely with backend engineers to ensure model enhancements are integrated directly into the product rather than remaining in research notebooks. This remote opportunity offers the chance to influence customer-facing AI capabilities, develop automated evaluation systems, and build new predictive models for expansion, cross-selling, forecasting, and marketing. You will thrive in a product-focused environment that values independent problem-solving, rigorous experimentation, and measurable business impact.

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 to ensure predictions remain accurate and relevant to business decisions.

Statistical Analysis and Backtesting: Develop time-based backtesting methodologies and fixed test sets to evaluate models rigorously and prevent data leakage from future information.

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 to strengthen model performance.

Production Implementation: Translate analytical findings and model improvements into production-ready changes within the existing Go-based product codebase, ensuring that work extends beyond experimentation and notebooks.

Model Monitoring and Automation: Within the first three to six months, automate evaluation processes and implement monitoring mechanisms that detect declines in recall, label quality, and product usage signals before they affect customers.

Initial Model Assessment: During the first 30 days, conduct recall analysis for every existing customer using fixed test sets and implement improvements identified through the analysis.

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 for customers and stakeholders without a machine learning background.

Data Confidentiality: Protect sensitive customer information, internal queries, training datasets, and analytical results when communicating findings or discussing model performance.

Cross-Functional Collaboration: Work closely with backend engineering colleagues, particularly the Principal Backend Engineer, to maintain clear ownership boundaries and ensure prediction quality aligns with the wider product architecture.

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 based on observed performance and real-world outcomes.

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, such as churn prediction, retention, propensity modeling, customer health scoring, or segmentation.

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 that can undermine model reliability.

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 to investigate model behavior and support feature development.

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, but the ability to deliver production-level changes is essential.

Model Evaluation and Error Analysis: Experience investigating individual predictions, identifying root causes of errors, and translating findings into concrete model or data improvements.

Analytical Problem-Solving: Ability to independently structure ambiguous problems, develop testable hypotheses, conduct rigorous experiments, and implement practical solutions.

Product Engineering Mindset: Experience working within a product-based organization and understanding how machine learning systems support real customer workflows and business outcomes. Candidates must currently be working at a product-based organization.

Communication Skills: Ability to explain technical findings, statistical concepts, and model limitations clearly to non-technical stakeholders while maintaining appropriate confidentiality.

Collaboration: Strong interpersonal skills and the ability to work effectively with backend engineers and other cross-functional stakeholders.

Additional Advantages: A public GitHub profile, relevant machine learning side projects, or a technical blog demonstrating practical expertise and initiative would be a strong plus.

Benefits

Remote Work: Full-time remote opportunity for candidates based in India.

Competitive Engagement Structure: Full-time contractor arrangement with a US-based organization, with no deductions made by the hiring company from the agreed compensation.

High-Impact Responsibilities: Direct ownership of machine learning prediction quality and the opportunity to influence customer-facing product decisions.

Production-Focused Machine Learning: Work on live models and real business outcomes rather than limiting your contribution to research, prototypes, or experimental notebooks.

Advanced Analytical Challenges: Solve practical problems involving statistical evaluation, data leakage, model accuracy, customer behavior, and prediction reliability.

AI and Product Innovation: Contribute to new predictive capabilities for customer expansion, cross-selling, forecasting, and marketing.

Cross-Functional Collaboration: Partner closely with backend engineering to integrate model improvements into production systems and deliver measurable product value.

Technical Autonomy: Independently investigate complex problems, design experiments, and drive improvements from analysis through implementation.

Professional Growth: Strengthen your expertise in applied machine learning, statistical modeling, production engineering, and automated model monitoring.

Transparent Interview Process: Participate in focused technical discussions covering applied machine learning and statistics, model evaluation and error analysis, and SQL and product engineering.

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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