#7 - Senior Data Scientist & AI - Databricks
Summary
Senior data scientist with Muttdata, a remote-first ML consultancy, working embedded with a multinational beverage client in Mexico City. Builds AI features — recommendations, personalization, segmentation, churn prediction, notification optimization — for a large B2B app used across 9 countries, using Python, SQL, Databricks, and Spark.
🚀 Join Our Remote Data Products & Machine Learning Startup! 🚀
At Muttdata, we build innovative Data Products and Machine Learning solutions that help companies solve complex business challenges. As a fast-growing, remote-first startup, we're passionate about technology, collaboration, and continuous learning.
This opportunity is with a leading multinational beverage company based in Mexico City.
We are looking for a Data Scientist to join our team 🐶🚀. You'll be part of a multidisciplinary team focused on building the "Perfect Order", developing Machine Learning and Artificial Intelligence solutions that enhance the digital customer journey within a large-scale B2B application used by hundreds of thousands of customers across 9 countries.
In this role, you'll work on innovative AI-driven products, including recommendation and personalization systems, customer segmentation, digital behavior analysis, notification optimization, and churn prediction models.
You'll collaborate closely with Product, Engineering, and Business teams to design scalable, data-driven solutions that improve customer engagement, optimize user experiences, and generate measurable business impact.
🚀 What We Do
- Leveraging our expertise, we build modern Machine Learning systems for demand planning and budget forecasting.
- Developing scalable data infrastructures, we enhance high-level decision-making, tailored to each client.
- Offering comprehensive Data Engineering and custom AI solutions, we optimize cloud-based systems.
- Using Generative AI, we help e-commerce platforms and retailers create higher-quality ads, faster.
- Building deep learning models, we enhance visual recognition and automation for various industries, improving product categorization, quality control, and information retrieval.
- Developing recommendation models, we personalize user experiences in e-commerce, streaming, and digital platforms, driving engagement and conversions.
🌟 Our Partnerships
- Amazon Web Services
- Astronomer
- Databricks
🌟 Our Values
- 📊 We are Data Nerds
- 🤗 We are Open Team Players
- 🚀 We Take Ownership
- 🌟 We Have a Positive Mindset
Responsibilities 🤓
- Design, develop, and implement Machine Learning and AI solutions to enhance the digital customer experience.
- Build and optimize recommendation and personalization models to deliver relevant products, content, and actions to users.
- Develop customer segmentation and behavioral analysis models to better understand user preferences and interactions.
- Design predictive models for customer churn detection and prevention.
- Work on notification orchestration and prioritization models to optimize customer engagement throughout the digital journey.
- Perform Exploratory Data Analysis (EDA), statistical analysis, and feature engineering to identify patterns and opportunities.
- Develop scalable Machine Learning solutions capable of processing large volumes of data across multiple markets.
- Collaborate with cross-functional teams to translate business challenges into Machine Learning and AI solutions.
- Evaluate model performance, validate results, and continuously improve predictive accuracy and business impact.
- Communicate insights, model outcomes, and actionable recommendations to technical and non-technical stakeholders.
Required Skills 🚀
- Hands-on experience developing and implementing Machine Learning and Artificial Intelligence solutions.
- Strong proficiency in Python and SQL.
- Solid knowledge of statistics, Exploratory Data Analysis (EDA), and feature engineering.
- Strong understanding of Machine Learning algorithms, predictive modeling, and statistical techniques.
- Experience working with recommendation systems, personalization, customer segmentation, digital behavior analysis, churn prediction, or similar use cases.
- Experience developing scalable Machine Learning and AI solutions.
- Familiarity with Databricks and Apache Spark.
- Ability to analyze complex datasets and translate findings into actionable business insights.
- Strong analytical thinking, problem-solving, and communication skills.
- Ability to collaborate effectively with multidisciplinary teams and communicate technical results to business stakeholders.
Nice to Have 😉
- Experience working with Large Language Models (LLMs).
- Knowledge of AI agents and agentic systems.
- Familiarity with MLOps practices and tools.
- Experience deploying and maintaining Machine Learning and AI models in production environments.
- Knowledge of job orchestration, compute resources, model deployment, monitoring, and maintenance.
- Experience working with event data, user tracking, and application tagging.
- Experience collaborating closely with Product, Engineering, and Business teams to develop data-driven products.
🎁 Perks
- Remote-first culture – work from anywhere! 🌍
- AWS, DBT, Google Cloud, Azure & Databricks certifications fully covered
- Birthday off + an extra vacation week (Mutt Week! 🏖️)
- Referral bonuses – help us grow the team & get rewarded!
- Maslow: Monthly credits to spend in our benefits marketplace.
- ✈️🏝️ Annual Mutters' Trip – an unforgettable getaway with the team!
Skills
As published by lever · 11 questions · 7 written answers
Basics
Resume/CV, Full name, Email, Phone, Current location, Current company, LinkedIn URL, GitHub URL, Other URL
Pick from a list (4)
- Do you currently reside in Mexico or Latin America? (This is a mandatory requirement for this role. If you do not currently reside in Latin America or Mexico, your application will not be considered.)
- Where did you hear about us? optional
- English Level* optional
- Working mode optional
Written answers (7)
- Describe about your relevant experience in the role optional
- Tell us about a production data/ML pipeline you've orchestrated on Databricks: how did you structure task dependencies and handle failure modes? optional
- Do you have hands-on experience with Databricks Asset Bundles (DABs) for CI/CD deployments across dev/qa/prd? Walk us through an example. optional
- How many years of experience do you have managing models and artifacts in MLflow + Unity Catalog (versioning, aliases, rollback)? optional
- Do you have experience with FinOps practices (cost tagging/monitoring) or AIOps/observability integration on cloud data platforms? optional
- Study/Degree optional
- Gross net salary expectations (USD) optional



