We are seeking a Senior Full-Stack Machine Learning Engineer who thrives at the intersection of software engineering and data science. In this role, you will be the bridge between raw data and product impact. You won鈥檛 just be training models in a vacuum; you will be architecting the data pipelines that feed them and the production systems that serve them.
This is a hybrid role for a builder who thinks like a scientist. You will not only build the engines (ML Engineering) but also act as the navigator (Data Science), using data to tell us where the product should go next.
What You'll Do: Key Responsibilities
End-to-End ML Lifecycle: Design, develop, and deploy production-grade ML models using Python and Spark. You will own the full cycle from feature engineering to model monitoring.
Data Architecture & Pipelines: Build and maintain robust data pipelines within our Databricks environment.
Exploratory Data Analysis (EDA) & Discovery: Dive deep into large datasets to uncover hidden patterns, anomalies, and opportunities. You don鈥檛 just process data; you interpret what it says about our users.
Statistical Rigor & Hypothesis Testing: Design and execute rigorous A/B tests and multivariate experiments. You will be responsible for calculating sample sizes, p-values, and confidence intervals to ensure product changes are statistically significant.
Metric Definition: Work with stakeholders to define what "success" looks like. You will translate vague business questions (e.g., "Why is churn increasing?") into measurable data science problems.
Predictive Modeling & Insights: Beyond production pipelines, you will create ad-hoc models to forecast business trends and provide actionable insights that influence the product roadmap.
Data Storytelling: Communicate complex findings through high-quality visualizations and dashboards (using tools like Tableau, PowerBI, or Databricks SQL). You can tell a "story" with data to convince leadership of a strategic direction.
Product Impact: Collaborate with Product Managers to translate business goals into technical ML objectives. You will be responsible for defining and moving key performance indicators (KPIs) through algorithmic improvements.
Collaborative Engineering: Work as a peer within the engineering team, applying software best practices (unit testing, code reviews, design docs) to the ML stack.
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