Data Scientist

About Mutinex

Mutinex is an AI-driven growth platform partnering with over 100 international brands. Their cutting-edge measurement technology has analyzed upwards of $9 billion in media expenditures and consistently yields superior results compared to traditional methods.

The platform, GrowthOS, leverages advanced data science fused with user-friendly software, empowering marketers to comprehend the effectiveness of previous decisions and optimize future budget allocation.

Role Overview

The Data Scientist role resides within the Model Scale team, focusing on ensuring the accuracy, reliability, and actionable value of model outputs for every client. Responsibilities include adapting modeling technology to new and updated datasets, monitoring deployed models, diagnosing irregularities, and enhancing automated testing and diagnostics supporting model deployment.

This position involves full-cycle engagement from preparing complex customer data to generating production-ready model outputs. It requires collaboration with Data Science for methodologies and feature development, Engineering for platform resilience and automation, and customer teams for contextual understanding of data.

Key Responsibilities

• Implement robust customer modeling by applying Mutinex’s modeling tech to fresh and updated customer datasets while maintaining stringent standards for accuracy, consistency, and timely delivery.
• Oversee model operations by scrutinizing inputs, outputs, and overall performance in live environments. Detect anomalies, concept drift, and emerging risks proactively to prevent customer impact.
• Investigate and diagnose unexpected model behaviors by linking them to data shifts, configuration changes, implementation issues, or methodology faults, while exercising prudent decisions on fixes or escalation.
• Automate recurring tasks by developing reusable data validations, model tests, exploratory analyses, and diagnostic workflows that accelerate and stabilize delivery pipelines.
• Enhance observability through the creation of clear visualizations, monitoring dashboards, and alert mechanisms to facilitate understanding and troubleshooting of model behavior.
• Collaborate closely with Data Science to assist in validating models, engineering features, extracting relevant variables, and refining methodologies informed by customer deployments.
• Work alongside Engineering teams to improve data access, workflow automation, monitoring systems, and overall infrastructure reliability that underpin model functions.
• Contribute to team processes by promoting peer reviews, documentation, shared standards, and quality control gates ensuring knowledge and quality are team-wide assets.
• Communicate findings transparently, articulating uncertainties, risks, and trade-offs to both technical colleagues and non-technical stakeholders.

Candidate Profile

• Experience in data science, applied statistics, analytics, ML operations, or related fields engaging with real-world datasets and recurring analytic workflows.
• Strong analytical grounding in statistical uncertainty, hypothesis testing, Bayesian methods, and machine learning techniques with an ability to critically assess one’s conclusions.
• Proficiency in Python and SQL for complex data exploration, transformation, validation, and turning repetitive tasks into maintainable tools.
• Engineering mindset for coding: use of version control, test writing, thorough peer-review participation, and delivering maintainable code.
• Meticulous about quality assurance and honest result validation embracing peer feedback and testing as necessities.
• Systematic diagnostic skills to isolate issues and discern whether problems arise from data inconsistencies, configuration errors, engineering faults, or modeling limitations.
• Capability to extract meaningful structure and signals from messy, incomplete, or inconsistent customer data.
• Understanding of production machine learning principles including reliable execution, monitoring, error recovery, and trustworthy output generation.
• Focus on system improvements by automating repeated manual work and mitigating recurring issues through reusable checks, documentation, and better processes.
• Clear communication skills suitable for explaining complex technical matters and uncertainty without jargon.
• Effective interdisciplinary collaboration with Data Science, Engineering, Product, and customer teams, engaging proper expertise timely.
• Curiosity about varied business domains and the linkage of model outputs to decision-making contexts.
• Ownership mindset: early risk detection, sustained problem resolution, and active contribution to shared knowledge and standards.
• Utilization of AI as an acceleration tool for exploration, analysis, documentation, and automation while maintaining validation rigor.

Additional Information

Experience with time-series modeling, causal inference, marketing analytics, cloud platforms like GCP, dashboard solutions, or production ML environments is advantageous but not mandatory.

Team Environment

The Data Science group is vital to Mutinex’s customer value proposition. The Model Scale team integrates modeling technology with real client data, ensuring quality, diagnosing issues, and feeding insights into methodology and platform enhancements.

This collaborative environment involves Data Scientists, Machine Learning Engineers, software developers, and client-facing teams. Roles are distinct but work tightly together with explicit ownership and shared problem-solving.

Career Outlook

This role is centered on building scalable systems, standards, and tools that enable advanced marketing measurement delivery across many datasets and clients. There is potential growth towards deeper specialization in feature engineering, model development, or machine learning engineering.

The company prioritizes mindset, investigative approach, and continuous improvement over conventional career paths, looking for candidates passionate about tackling complex data challenges and creating dependable customer outcomes.