Data Scientist

About Mutinex

Mutinex is a cutting-edge AI-driven growth platform utilized by over 100 global brands. Our measurement technology has processed more than $9 billion in media expenditures and consistently surpasses traditional methods.

We focus on solving complex marketing challenges, specifically determining what strategies truly work. Our flagship product, GrowthOS, integrates sophisticated data science with user-friendly software to help marketers evaluate past decisions' effectiveness and optimize future investments.

Role Overview

As a member of the Model Scale team, you will ensure our models deliver precise, dependable, and actionable results for every client. This involves applying our modeling technology to both new and updated datasets, supervising models in production, investigating anomalies, and enhancing automated testing and diagnostics.

This position requires engagement beyond simple data analysis; you will be involved throughout the entire pipeline—from processing raw and diverse customer data to producing reliable model outputs—collaborating closely with Data Science, Engineering, and customer-facing teams.

The ideal candidate combines deep analytical capabilities with pragmatic problem-solving, possesses strong statistical judgment, technical prowess for data exploration and automation, and disciplined operational execution in a probabilistic environment.

Key Responsibilities

• Implement accurate and reliable customer-facing models by applying Mutinex’s modeling technologies to evolving datasets while ensuring timeliness and consistency.
• Continuously monitor production model quality by assessing inputs, outputs, and performance metrics to detect anomalies, data drift, and emerging risks before impacting customers.
• Systematically diagnose unusual model behaviors by tracing issues to data changes, configuration, implementation, or methodology, and apply sound judgment regarding remediation or escalation.
• Develop automation tools for recurring tasks including data verification, model testing, exploratory analysis, and diagnostics to improve delivery speed and reliability.
• Enhance observability through clear visualizations, monitoring dashboards, and alert systems to facilitate understanding and investigation of model behavior.
• Collaborate with Data Science teams to validate models, engineer features, extract relevant data, and refine methodologies from real deployment insights.
• Coordinate with Engineering to improve data accessibility, automate workflows, monitor systems, and enhance production system reliability underlying our models.
• Contribute to team processes by participating in peer reviews, documentation efforts, establishing shared standards, and implementing quality controls to ensure knowledge retention and consistent output.
• Communicate findings, uncertainties, potential risks, and trade-offs transparently and in an accessible manner to both technical and non-technical stakeholders.

Desired Qualifications and Skills

• Experience in data science, applied statistics, analytics, ML operations, or a related discipline with proficiency handling real-world data and routine analytical workflows.
• Strong analytical foundation including understanding of statistical uncertainty, hypothesis testing, Bayesian reasoning, and common machine learning approaches with an ability to critically evaluate conclusions.
• Proficient programming skills in Python and SQL to explore, transform, validate complex datasets, and develop reusable automation tools.
• Engineering mindset for coding: version control, writing comprehensive tests, adherence to code reviews, and producing maintainable software components.
• Meticulous attention to detail with rigorous validation practices and commitment to peer-reviewed quality standards.
• Methodical problem-solving skills suitable for identifying and isolating errors stemming from data, configuration, engineering defects, or modeling constraints.
• Ability to work effectively with incomplete, inconsistent, or messy customer datasets, extracting meaningful patterns and signals.
• Understanding of production machine learning environments requiring reliable, monitorable, fault-tolerant, and trustworthy model outputs.
• Focus on systemic improvements by converting repetitive manual tasks into automated processes, reusable tests, documentation, and improved workflows.
• Clear communicator capable of distilling complex technical insights and uncertainties into understandable terms without jargon.
• Collaborative work style engaging effectively with cross-disciplinary teams including Data Science, Engineering, Product, and customer-facing groups.
• Curiosity about customers’ business contexts, linking model results directly to decision-making processes.
• Proactive ownership of tasks including early risk identification, thorough problem resolution, and continuous contribution to team knowledge and standards.
• The judicious use of AI tools to accelerate exploratory analysis, documentation, and automation while maintaining high validation standards.

Additional Information

Experience with time-series modeling, causal inference, marketing analytics, cloud platforms such as Google Cloud Platform, dashboard development tools, or production ML systems is advantageous but not mandatory.

Team Environment: Data Science is integral to Mutinex’s value proposition. Model Scale is dedicated to integrating and operationalizing models with actual customer data, maintaining high quality, troubleshooting issues, and feeding insights back to methodology and platform enhancements.

This role involves close collaboration across Data Science, Machine Learning and Software Engineering, and customer-facing departments, emphasizing explicit ownership and collaborative problem-solving rather than handoff.

Career Perspective: The position goes beyond repetitive analysis to building scalable systems, standards, and tools that deliver advanced marketing measurement reliably. Opportunities exist to delve deeper into feature engineering, model development, or machine learning engineering as expertise develops.

We prioritize candidates who demonstrate thoughtful inquiry, practical investigative skills, and a drive to continuously improve their work environment over conventional career trajectories. If tackling complex data challenges and delivering trustworthy modeling outcomes excite you, we encourage you to connect with us.