Description review
#5 - Data Scientist Semi Senior - Causal Inference
Mutt Data · Remote · back to the listing
HR standards
67/100
needs work
Title ↔ description
82/100
solid
Reads as
Data Scientist
100% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
How others title the same work
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What the listing never says
- 40 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
🚀 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.
We are looking for an analytical, curious, and business-minded Data Scientist Semi Senior 🐶🚀 to join one of our key engagements with a leading beverage company in Mexico. You will be the go-to person for measuring what really works in growth and marketing: designing experiments, estimating causal impact, and turning the results into decisions that drive customer acquisition, retention, and revenue.
This role sits at the intersection of statistics, economics, and marketing. You will work closely with marketing, growth, and commercial stakeholders, as well as with Data Engineers and Analytics Engineers, to move beyond correlations and answer questions like "Did this campaign actually cause an increase in sales?" or "Which promotions and channels deserve more budget?". A strong analytical profile, methodological rigor, and the ability to communicate findings to non-technical audiences are essential. A background in Economics (or similar quantitative fields) is highly valued.
🚀 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
🔍 Curious about what we’re up to? Check out our case studies and dive into our blog post to learn more about our culture and the exciting projects we’re working on! 🚀
Responsibilities 🤓
• Design, run, and analyze A/B tests and multivariate experiments (sample size and power calculations, randomization, guardrail metrics, interpretation of results).
• Apply causal inference techniques (Difference-in-Differences, Synthetic Control, Propensity Score Matching, Instrumental Variables, Regression Discontinuity, uplift modeling) to estimate the impact of marketing campaigns, promotions, pricing, and loyalty initiatives when randomization is not possible.
• Measure and optimize marketing performance: incrementality, attribution, ROI/ROAS, customer lifetime value (CLV), and marketing mix modeling (MMM).
• Translate business questions from growth and marketing teams into well-defined analytical problems and experimental designs.
• Develop analyses, features, and models on Databricks (notebooks, Spark, Delta Lake), collaborating with Data Engineers and Analytics Engineers on data pipelines and analytical datasets.
• Build predictive and segmentation models (churn, propensity, customer segmentation) that support targeting and personalization strategies.
Required Skills
• 3+ years of experience in Data Science, Applied Statistics, Econometrics, or similar analytical roles.
• Hands-on experience with Databricks (notebooks, Spark/PySpark, Delta Lake) – required.
• Solid, hands-on experience designing and analyzing A/B tests and online/offline experiments.
• Strong knowledge of causal inference methods and their assumptions, limitations, and practical application (DiD, Synthetic Control, Matching, IV, uplift, etc.).
• Strong foundations in statistics and econometrics (hypothesis testing, regression, Bayesian and frequentist approaches, time series).
• Proficiency in Python (pandas, PySpark, statsmodels, scikit-learn) and advanced SQL.
• Experience applying data science to growth, marketing, or commercial problems (campaign measurement, pricing, promotions, customer analytics).
Nice to Have Skills 😉
• Degree in Economics, Econometrics, Statistics, or related quantitative fields (ideally with a focus on applied microeconomics or causal inference).
• Experience in CPG, retail, consumer goods, or beverage industries.
• Experience with Marketing Mix Modeling (e.g., Meridian, Robyn, PyMC-Marketing) and media attribution.
• Experience with causal libraries (DoWhy, EconML, CausalML) and Bayesian modeling (PyMC, Stan).
• Experience with MLflow and Databricks workflows/jobs.
🎁 Perks
• 🌍 Remote-first culture – work from anywhere!
• 🚀 In-Company English Lessons.
• 💪 Wellhub or sports club stipend to stay active
• 🚀 AWS, DBT, Google Cloud, Azure & Databricks certifications fully covered
• 🍕 Food credits via Pedidos Ya – because great work deserves great food.
• 🎂 Birthday off + an extra vacation week (Mutt Week! 🏖️)
• 🤝 Referral bonuses – help us grow the team & get rewarded!
• ✈️🏝️ Annual Mutters' Trip – an unforgettable getaway with the team!
• 👶 Monthly Childcare Reimbursement – Because supporting families matters too
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.
We are looking for an analytical, curious, and business-minded Data Scientist Semi Senior 🐶🚀 to join one of our key engagements with a leading beverage company in Mexico. You will be the go-to person for measuring what really works in growth and marketing: designing experiments, estimating causal impact, and turning the results into decisions that drive customer acquisition, retention, and revenue.
This role sits at the intersection of statistics, economics, and marketing. You will work closely with marketing, growth, and commercial stakeholders, as well as with Data Engineers and Analytics Engineers, to move beyond correlations and answer questions like "Did this campaign actually cause an increase in sales?" or "Which promotions and channels deserve more budget?". A strong analytical profile, methodological rigor, and the ability to communicate findings to non-technical audiences are essential. A background in Economics (or similar quantitative fields) is highly valued.
🚀 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
🔍 Curious about what we’re up to? Check out our case studies and dive into our blog post to learn more about our culture and the exciting projects we’re working on! 🚀
Responsibilities 🤓
• Design, run, and analyze A/B tests and multivariate experiments (sample size and power calculations, randomization, guardrail metrics, interpretation of results).
• Apply causal inference techniques (Difference-in-Differences, Synthetic Control, Propensity Score Matching, Instrumental Variables, Regression Discontinuity, uplift modeling) to estimate the impact of marketing campaigns, promotions, pricing, and loyalty initiatives when randomization is not possible.
• Measure and optimize marketing performance: incrementality, attribution, ROI/ROAS, customer lifetime value (CLV), and marketing mix modeling (MMM).
• Translate business questions from growth and marketing teams into well-defined analytical problems and experimental designs.
• Develop analyses, features, and models on Databricks (notebooks, Spark, Delta Lake), collaborating with Data Engineers and Analytics Engineers on data pipelines and analytical datasets.
• Build predictive and segmentation models (churn, propensity, customer segmentation) that support targeting and personalization strategies.
Required Skills
• 3+ years of experience in Data Science, Applied Statistics, Econometrics, or similar analytical roles.
• Hands-on experience with Databricks (notebooks, Spark/PySpark, Delta Lake) – required.
• Solid, hands-on experience designing and analyzing A/B tests and online/offline experiments.
• Strong knowledge of causal inference methods and their assumptions, limitations, and practical application (DiD, Synthetic Control, Matching, IV, uplift, etc.).
• Strong foundations in statistics and econometrics (hypothesis testing, regression, Bayesian and frequentist approaches, time series).
• Proficiency in Python (pandas, PySpark, statsmodels, scikit-learn) and advanced SQL.
• Experience applying data science to growth, marketing, or commercial problems (campaign measurement, pricing, promotions, customer analytics).
Nice to Have Skills 😉
• Degree in Economics, Econometrics, Statistics, or related quantitative fields (ideally with a focus on applied microeconomics or causal inference).
• Experience in CPG, retail, consumer goods, or beverage industries.
• Experience with Marketing Mix Modeling (e.g., Meridian, Robyn, PyMC-Marketing) and media attribution.
• Experience with causal libraries (DoWhy, EconML, CausalML) and Bayesian modeling (PyMC, Stan).
• Experience with MLflow and Databricks workflows/jobs.
🎁 Perks
• 🌍 Remote-first culture – work from anywhere!
• 🚀 In-Company English Lessons.
• 💪 Wellhub or sports club stipend to stay active
• 🚀 AWS, DBT, Google Cloud, Azure & Databricks certifications fully covered
• 🍕 Food credits via Pedidos Ya – because great work deserves great food.
• 🎂 Birthday off + an extra vacation week (Mutt Week! 🏖️)
• 🤝 Referral bonuses – help us grow the team & get rewarded!
• ✈️🏝️ Annual Mutters' Trip – an unforgettable getaway with the team!
• 👶 Monthly Childcare Reimbursement – Because supporting families matters too