Senior Ml Ops Engineer - Remote & Impactful

Agileengine, Llc.

Santa Fe Capital

Remote

ARS 92,241,000 - 138,361,000

Full time

3 days ago
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Benefits offered by this job

10 days PTO
USA Holidays
100% remote

Job summary

Prometeo Talent, acting on behalf of a US-based client, seeks a Data Analyst (Machine Learning) to turn large consumer and retail data into actionable metrics. You will work with SQL, Python, and ML models to derive insights and support business decisions in English.

Role is 100% remote across LATAM with flexible experience levels. Candidates should have strong data manipulation skills, a numbers mindset, and the ability to communicate results clearly to stakeholders.

Qualifications

  • 0–4 years in Data Analysis, Analytics, or similar roles; entry-level welcome with coursework or internships.
  • Advanced English (written and spoken) is required.
  • Strong SQL skills with complex joins and aggregations.
  • Python for data cleaning, manipulation, and processing using pandas.

Responsibilities

  • Write, optimize, and run SQL queries to extract data from relational databases and warehouses.
  • Develop, validate, and apply machine learning or statistical models to forecast trends.
  • Slice and aggregate data to compute key metrics like market share and trends.
  • Create and maintain data tables and dashboards for stakeholders.
  • Audit data outputs and ensure accuracy before reporting.
  • Segment data using filters and conditional logic for cohorts.
  • Parse and categorize unstructured text data and summarize results clearly.

Skills

SQL
Python
Pandas
Machine learning
Data analysis
English communication

Education

Bachelor's degree in a related field

Tools

Snowflake
BigQuery
Redshift

Job description

Machine Learning Analyst | 100% Remoto | LATAM

What do we offer?

Prometeo Talent is a recruitment agency with a strong presence across the Americas and Europe. We specialize in connecting companies with exceptional professionals across data, analytics, and business functions.

We have partnered with a U.S.-based company specialized in marketing effectiveness measurement. They help organizations better understand consumer behavior, purchasing trends, brand performance, and market dynamics through data-driven insights.

We are looking for a Data Analyst (Machine Learning) to join our client's global team.

This role is ideal for analytical and data-driven professionals who enjoy working with large-scale consumer and retail datasets, bridging the gap between complex database structures and business strategy, and turning raw transactional or behavioral data into clear, actionable metrics.

The ideal candidate combines solid SQL and Python skills, a strong "numbers sense", attention to detail, and the ability to translate technical results into clear summaries for stakeholders in English. The position is open to a range of experience levels, from recent graduates with a strong quantitative background to mid-level analysts looking to deepen their technical data manipulation skills.

What You Will Do

  • Write, optimize, and execute SQL queries to extract data from relational databases and cloud data warehouses.
  • Develop, run, and validate machine learning, predictive, or statistical matching models to uncover deeper consumer behaviors and forecast trends.
  • Slice, unpivot, and aggregate raw transactional or panel data to calculate core business metrics such as household penetration, market share, and purchasing trends.
  • Build and maintain clean, structured data tables and views that feed into recurring tracking reports and stakeholder dashboards.
  • Review query outputs, perform data audits, and double-check logic to ensure accuracy and data integrity before insights are finalized.
  • Use conditional logic and filtering techniques to segment data and isolate specific tracking cohorts.
  • Clean, parse, and categorize unstructured or semi-structured text data.
  • Translate technical data structures and query results into clear written summaries, bullet points, or data tables for stakeholders in English-speaking environments.

Requirements

  • 0 to 4 years of experience in Data Analysis, Business Analytics, or similar roles. Entry-level candidates with strong relevant coursework, personal data projects, or internships are welcome, as well as mid-level professionals with a proven track record of querying databases to drive business insights.
  • Advanced English level (written and spoken).
  • Foundational to intermediate SQL skills, including complex JOIN logic, standard aggregations, and CASE WHEN conditional logic.
  • Python scripting for data cleaning, manipulation, and processing, with proficiency in pandas for complex multi-step transformations.
  • Familiarity with modeling libraries such as scikit-learn or statsmodels to build and evaluate predictive or statistical matching models.
  • Experience working with messy text fields: cleaning, parsing, filtering, and pattern matching within string variables.
  • Solid understanding of relational database design, including how tables connect, primary/foreign keys, and data normalization.
  • Bachelor's degree in Business Analytics, Statistics, Mathematics, Computer Science, Economics, Marketing, or a related field with coursework covering database management or quantitative data analysis.

Nice to Have

  • Experience working with consumer, retail, panel, or transactional datasets.
  • Familiarity with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift).
  • Exposure to marketing analytics, consumer insights, or market measurement environments.
  • Experience building data tables or views that feed dashboards and recurring reports.

Why Join Us?

  • 10 days of PTO to recharge wherever you choose.
  • Enjoy USA Holidays.
  • 100% remote work—design the work-life balance that fits you best.
  • Work with real consumer and market data that influences strategic business decisions.
  • Collaborate with international teams and stakeholders.
  • Develop your expertise in SQL, Python, and machine learning while working on impactful projects.
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