Job Specification: Model Builder (Data Science & Machine Learning)

🎯 Role Definition

The Model Builder is the architect of our predictive capabilities, sitting at the critical intersection of data science, engineering, and business strategy. This role is responsible for the end-to-end creation of statistical and machine learning models that solve our most complex challenges. You will be the go-to expert for transforming vast datasets into actionable insights, building the intelligent systems that power personalization, optimize operations, and uncover new market opportunities. Success in this role requires a powerful combination of quantitative rigor, advanced coding skills, and a deep understanding of what drives business value.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Analyst
Business Intelligence (BI) Analyst
Junior Data Scientist
Software Engineer (with a data focus)

Advancement To:

Senior Model Builder / Senior Data Scientist
Machine Learning Engineer
Data Science Manager or Team Lead
Principal Data Scientist

Lateral Moves:

Data Engineer
Analytics Consultant
AI/ML Product Manager

Core Responsibilities

Primary Functions

Own the complete lifecycle of machine learning models, from initial problem framing and data discovery through to deployment, monitoring, and continuous improvement.
Design, develop, and rigorously test predictive models using a variety of machine learning algorithms to address key business problems like customer churn, lifetime value, fraud detection, and demand forecasting.
Translate ambiguous business questions and high-level strategic objectives into well-defined machine learning problems and technical project plans.
Conduct comprehensive exploratory data analysis, data cleansing, and feature engineering to construct robust, high-quality datasets for model training.
Evaluate, compare, and select the most appropriate algorithms (e.g., gradient boosting, neural networks, time-series models, NLP) based on the specific use case and data characteristics.
Implement robust validation and back-testing frameworks to ensure model accuracy, generalizability, and stability before and after deployment.
Collaborate closely with MLOps and Data Engineering teams to deploy models into production environments, ensuring they are scalable, efficient, and integrated with our existing systems.
Proactively monitor the performance of deployed models in real-time, identifying concept drift or data drift and triggering retraining or recalibration as needed.
Clearly and effectively communicate complex modeling methodologies, performance metrics, and business implications to a diverse audience, including executive leadership and non-technical stakeholders.
Author production-quality, well-documented, and maintainable code, primarily in Python, for all aspects of the modeling process.
Research and experiment with cutting-edge machine learning techniques, open-source tools, and academic papers to drive innovation within the team.
Partner with data engineers to specify and co-develop the data pipelines and infrastructure required for efficient model training, feature generation, and inference.
Develop and apply model interpretability and explainability techniques (e.g., SHAP, LIME) to foster trust and understanding of "black-box" models among business users.
Engage directly with product managers and business unit leaders to identify and scope new opportunities where predictive analytics can generate significant business impact.
Perform deep-dive root cause analysis on model prediction failures to understand their limitations and inform future model iterations.
Create and maintain thorough documentation for every model, covering the underlying theory, data sources, assumptions, validation results, and deployment instructions.
Utilize cloud-based machine learning platforms (e.g., AWS SageMaker, Google AI Platform, Azure ML) for building and deploying models at scale.
Act as a subject matter expert on statistical modeling and machine learning best practices, mentoring junior analysts and data scientists.
Develop A/B testing or other controlled experimental designs to precisely measure the true impact and ROI of deployed models on business KPIs.
Ensure all modeling activities adhere to data privacy, security, and ethical AI guidelines, working with legal and compliance teams as needed.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis to provide quick-turnaround insights for pressing business questions.
Contribute to the organization's broader data governance, data quality, and data strategy initiatives.
Collaborate with business units to translate their strategic needs and pain points into tangible data science and engineering requirements.
Actively participate in sprint planning, retrospectives, daily stand-ups, and other agile ceremonies within the data science team.

Required Skills & Competencies

Hard Skills (Technical)

Advanced Programming: High-level proficiency in Python and its core data science libraries (Pandas, NumPy, Scikit-learn, Matplotlib); experience with deep learning frameworks like TensorFlow or PyTorch is a major plus.
Statistical & ML Expertise: Deep, intuitive understanding of a wide range of machine learning techniques (e.g., regression, classification, clustering, NLP, time-series forecasting) and the statistical theory behind them.
SQL and Databases: Mastery of SQL for complex querying, data extraction, and manipulation across large-scale relational and non-relational databases.
Cloud Computing: Hands-on experience with at least one major cloud platform (AWS, GCP, or Azure) and its associated ML/data services (e.g., S3, SageMaker, Redshift, BigQuery).
MLOps & Engineering Practices: Familiarity with modern software development practices, including version control (Git), containerization (Docker), and workflow orchestration (e.g., Airflow).
Data Visualization & Storytelling: Ability to use tools like Tableau, Power BI, or Python libraries to create compelling visualizations that tell a clear story with data.
Big Data Technologies: Practical experience working with distributed computing frameworks like Apache Spark or Dask for processing datasets that don't fit in memory.

Soft Skills

Strategic Problem-Solving: A natural curiosity and a structured, analytical approach to breaking down complex, ambiguous business problems into solvable, data-driven components.
Exceptional Communication: The rare ability to bridge the gap between technical and non-technical worlds, explaining complex models and their implications in simple, business-centric terms.
Business Acumen: A strong commercial mindset and an inherent ability to connect data science initiatives directly to business outcomes, ROI, and strategic goals.
Cross-Functional Collaboration: A highly collaborative and team-oriented spirit, with a proven track record of working effectively with partners in product, engineering, marketing, and finance.
Intellectual Humility & Detail-Orientation: Meticulous attention to detail in code, data, and analysis, combined with an open-mindedness to challenge one's own assumptions and learn from others.

Education & Experience

Educational Background

Minimum Education:

A Bachelor's Degree in a quantitative discipline is required.

Preferred Education:

A Master’s Degree or Ph.D. in a highly quantitative field is strongly preferred.

Relevant Fields of Study:

Computer Science
Statistics
Mathematics
Data Science
Economics
Physics or another hard science

Experience Requirements

Typical Experience Range:

3-5+ years of dedicated, hands-on experience building and deploying machine learning models in a corporate or commercial setting.

Preferred:

5+ years of experience with a demonstrable portfolio of successfully deployed models that have driven measurable business value. Experience leading complex modeling projects from conception to completion.