Description

We are seeking a driven and creative Data Scientist with a minimum of 3 years of experience in a data science or machine learning role, who will be instrumental in building predictive models, testing hypotheses, and providing statistically sound insights that enhance our lead generation capabilities. The Data Scientist will be part of our analytics team, working closely with IT, marketing, media buying and CRO teams to transform raw data into actionable insights, whether that means building real-time scoring models, investigating campaign performance, or helping the business distinguish signal from noise when making decisions.

The ideal candidate is someone who thrives on solving ambiguous problems, brings strong statistical intuition alongside machine learning skills, and can serve as a trusted analytical partner to stakeholders across the business. They're as comfortable building production ML pipelines as they are answering a media buyer's question about whether a source is actually underperforming or just experiencing variance. Experience in digital marketing, lead generation, or ad-tech is highly desirable.

Primary Responsibilities

Leverage agentic AI-assisted development tools throughout the workflow, from exploratory analysis and feature engineering to model development and pipeline building, to accelerate iteration speed while maintaining rigor and code quality
Design, develop, and deploy machine learning models to segment and score landing page visitors based on intent and lead quality, using behavioral signals such as scroll depth, click patterns, session duration, and form engagement.
Engineer predictive features from raw user behavior data to improve model accuracy and business relevance.
Build and maintain real-time scoring pipelines that enable dynamic postback signals to ad networks, improving campaign optimization speed and efficiency.
Collaborate with marketing and analytics teams to define quality/intent tiers and translate business logic into quantifiable model outputs.
Conduct exploratory data analysis and experimentation to uncover patterns in user behavior that correlate with downstream lead quality and conversion outcomes.
Continuously monitor, evaluate, and iterate on model performance, ensuring alignment with evolving business goals and traffic patterns.
Contribute to forecasting and budget projection initiatives as needed, supporting strategic planning with data-driven modeling.
Clearly communicate, document and present findings, methodologies, and model specifications for cross-functional, non-technical stakeholders.
Support data-driven decision-making by quantifying uncertainty, assessing statistical significance, and challenging assumptions when the data doesn't support them.
Identify and quantify relationships between behavioral signals, traffic sources, campaign attributes, and lead quality outcomes; distinguish meaningful correlations from spurious ones.
Serve as an analytical partner to media buyers and marketing teams: investigate campaign and source performance, validate hypotheses about lead quality, and provide statistically rigorous answers to business questions.

Requirements

3+ years of experience in data science, machine learning, or a related quantitative role.
Comfortable working in agentic AI-assisted development environments (e.g., Cursor, ClaudeCode, Antigravity) to accelerate model development, data exploration, and pipeline building, including the judgment to know when AI-generated outputs need manual verification or refinement.
Strong proficiency in Python, including ML libraries such as scikit-learn, XGBoost, LightGBM, or similar.
Solid experience with feature engineering, particularly from behavioral, clickstream, or event-based data.
Familiarity with deploying models in production environments; experience with real-time or near-real-time inference is a strong plus.
Strong foundation in statistical inference, hypothesis testing and root-cause analysis: can distinguish signal from noise, assess significance, and communicate uncertainty clearly.
Proficiency in SQL and experience working with large-scale datasets.
Understanding of classification, regression, and clustering techniques; experience with user segmentation or propensity modeling preferred.
Exposure to digital marketing concepts and KPIs (CPL, CVR, ROAS, etc.) is highly desirable.
Ability to translate vague business questions into testable hypotheses and structured analyses.
Excellent problem-solving skills with the ability to work through ambiguity and define structure where none exists.
Strong communication skills with the ability to explain technical concepts to non-technical stakeholders.
Team-oriented mindset with a collaborative and proactive

Data Scientist at CapsLock