Requirements

- 5+ years of applied ML experience with structured business data in production.
- Strong foundations in mathematics and statistics from a rigorous academic programme. An IIT or a similarly strong programme is preferred.
- Experience owning a production model used for business decisions, such as churn, retention, propensity, customer health, or segmentation.
- Strong understanding of classification, precision, recall, fixed test sets, and time-based evaluation.
- Ability to identify and fix data leakage, incorrect labels, and weak features.
- Experience working with messy CRM and product-usage data.
- Strong SQL skills and the engineering ability to ship changes inside a product codebase.
- Ability to work with Go application code. Python can be used for analysis, but improvements must reach production.
- Experience investigating individual prediction errors and explaining them to people without an ML background.
- Ability to independently drive unclear problems through investigation, testing, and implementation.
- A public GitHub profile, side project, or technical blog is a strong plus.
- Candidate currently working at a product based organisation is a must.

Responsibilities

- Improve existing churn, retention, and account segmentation models using customer data.
- Evaluate predictions against actual renewal, churn, expansion, and adoption outcomes.




- Build time-based backtests that prevent models from accessing future information.
- Investigate incorrect predictions account by account and identify gaps in labels, features, or source data.
- Ship improvements into the existing Go product, rather than stopping at analysis notebooks.
- Explain model performance and prediction errors to customers in explicit business terms.
- Keep internal queries and training datasets confidential when discussing results.
- In the first 30 days, run recall analysis for every existing customer using fixed test sets and ship improvements identified through that analysis.
- Within 3–6 months, automate evaluation and flag drops in recall, label quality, or product-usage signals before customers report them.
- Build and validate new models for expansion, cross-sell, forecasting, and marketing use cases.
- Work with the Principal Backend Engineer, owning prediction quality while they own the surrounding product.

Job Details

- Location: Remote.
- Employment: You will be employed as a full-time contractor with FunnelStory, a USA-based company. The company will not make any deductions from your agreed compensation.

Interview Process

- Applied ML and Statistics Discussion
- Model Evaluation and Error Analysis
- SQL and Product Engineering Discussion

📌 Senior Machine Learning Engineer (India)
🏢 ClanX
📍 India