Getsquire10mo ago

Data & Machine Learning Engineer

Buenos Aires · (buenos Aires)Contractormid

Data ScienceMachine Learning EngineerData & AI

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Quick Summary

Key Responsibilities

Director of Engineering Dig into appointment, revenue, and behavioral data to identify where shops are leaving money on the table: demand patterns, pricing gaps, retention leaks,

Technical Tools

Data ScienceMachine Learning EngineerData & AI

WHO WE ARE

SQUIRE is the leading business management system designed for the needs of barbers, shop owners, and their communities. We believe the pursuit of artistry and autonomy should not be restricted by the complexities of running a business. With SQUIRE, we provide custom-branded tools, resources, and guidance to help barbers of all stages and experience levels attract and retain more customers, efficiently manage their shop operations, and increase their revenue.

Founded in 2015, SQUIRE is trusted by more than 30,000 barbers across 5,000+ shops in more than a thousand cities worldwide. From streamlined booking and opening new shops to real-time earning dashboards and building lasting customer relationships, SQUIRE supports shop owners in seamlessly bridging the gap between their personal craft and business goals. SQUIRE enables barbers everywhere to unlock their full potential both as artists and as entrepreneurs.

For more information, please visit getsquire.com or download the SQUIRE app from the App or Play Store.

The Data & Machine Learning Engineer plays a critical role on SQUIRE's newly formed Revenue Intelligence team, building ML-driven features that directly and measurably increase client revenue.

This role combines deep analytical expertise with hands-on engineering to surface revenue opportunities within SQUIRE's appointment, behavioral, and transactional data, and translate them into deployed, production-grade models. Responsibilities include demand forecasting, dynamic pricing, churn and rebooking modeling, experimentation design, and partnering with Product and Engineering to determine where ML can drive the greatest business impact.

Director of Engineering

Dig into appointment, revenue, and behavioral data to identify where shops are leaving money on the table: demand patterns, pricing gaps, retention leaks, and utilization inefficiencies, and build dollar-ceiling analyses to prioritize what to build first.

Partner with Product, Growth, and Engineering to determine which problems should and shouldn't be solved with ML, and translate business problems into quantifiable models.

Design, train, and deploy pricing models, demand forecasts, churn and rebooking models, and other models identified through data analysis.

Own the full ML lifecycle: data exploration, feature engineering, modeling, deployment, experimentation, and iteration. Data Engineers handle the majority of upstream data pipelines.

Build feature pipelines and model-ready datasets; set up model monitoring and drift detection to identify degradation before it impacts revenue.

Design and run experiments including A/B tests, quasi-experiments, and difference-in-differences analyses, deploying to controlled cohorts before scaling, with success metrics tied directly to shop revenue uplift.

Build experimentation capability across the organization and participate in Agile processes including sprint planning, estimation, and retrospectives.

The duties and responsibilities outlined above are not a comprehensive list, and additional tasks may be assigned based on business needs.

3-5 years of experience in data or machine learning engineering, ideally within a product-oriented tech team

Business outcomes-first mindset; ability to look at a dataset and identify revenue opportunities, not just statistical patterns.

Strong applied ML skills (regression, classification, time series forecasting, clustering) with sound judgment on tool selection and a bias against over-engineering.

Hands-on proficiency in Python, SQL, and the modern ML stack (scikit-learn, XGBoost/LightGBM, pandas, etc.); capable of going from notebook to production without a handoff.

Deep expertise in experimentation and causal inference: A/B testing, quasi-experimental methods, post-release analysis, and interpretation of results in noisy real-world data.

Strong understanding of core SaaS metrics (ARR, MRR, NDR, LTV, activation, retention) and comfort operating without an existing playbook in a newly formed team.

English proficiency is a must; ability to clearly communicate ideas and collaborate with English-speaking team members.

Preferred: Experience with pricing optimization, demand forecasting, or revenue modeling in marketplace or transactional businesses; track record of shipping ML features that moved a business metric; familiarity with Looker, dbt, Snowflake, or similar analytics stacks.

Preferred: Based in Buenos Aires with availability to work on-site in our office in CABA two days a week (Tuesdays and Thursdays).

Experience with pricing optimization, demand forecasting, or revenue modeling — especially in marketplace or transactional businesses.

You’ve built ML features that shipped to real users and moved a business metric. Not just models that scored well offline.

Experience building data infrastructure that ML depends on: feature pipelines, monitoring, experimentation workflows.

Familiarity with Looker, dbt, Snowflake, or similar analytics stacks.

You’ve worked in a small team where you wore multiple hats and owned outcomes end-to-end.

Work on impactful, customer-facing features powered by machine learning

Help shape our AI-first engineering culture

Enjoy autonomy and ownership over your domain with the support of an exceptional engineering team

Competitive salary, equity, and benefits in a high-growth, mission-driven company