Mid-Level Machine Learning & Analytics Engineer

Building Engines; Belen (NM); On-site; USD 110,000 - 150,000; Full time

Job summary

JLL is seeking a Mid-Level Machine Learning & Analytics Engineer to bridge predictive modeling, data architecture, and business strategy. You will design end-to-end data pipelines, build predictive ML models, and create actionable analytics frameworks that drive core product decisions.

You will collaborate with Data Engineers, Product Managers, and Business Stakeholders to transform complex structured and unstructured data into scalable, production-grade ML models and interactive analytical

Qualifications

• 3-5 years hands-on experience in ML/analytics or data science.
• Bachelor’s degree or higher in CS, Statistics, Data Science, Mathematics, Engineering, or related field.
• Strong English communication skills across stakeholders.
• Experience deploying ML systems in live production environments.
• Experience managing feature extraction, hyperparameter tuning, model validation, and cross‑validation.
• Familiarity with dimensional schema architecture and enterprise data strategies.

Responsibilities

• Predictive & ML Modeling: build, evaluate, and fine-tune supervised/unsupervised models (e.g., churn, LTV, recommendations).
• Advanced Analytics & Experimentation: design A/B tests, hypothesis testing, and causal inference models.
• Data Engineering & Pipeline Development: construct ETL/ELT pipelines using SQL and Python to create feature-ready data.
• Analytics Engineering & BI: model data for dashboards with dbt; build star/snowflake schemas; enable self-service BI (Tableau/Looker/Power BI).
• Model Deployment & Monitoring: wrap models into APIs (FastAPI) and monitor data drift and pipeline health.
• Cross-Functional Translation: communicate methodologies and insights to non-technical leaders.

Skills

ML & Analytics

English communication

Production ML deployment

Education

Bachelor’s degree or higher in CS/Stats/DS/Math/Engineering

Tools

SQL

Python

dbt

Tableau

Looker

Power BI

Job description

JLL empowers you to shape a brighter way.

Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology for our clients. We are committed to hiring the best, most talented people and empowering them to thrive, grow meaningful careers and to find a place where they belong. Whether you’ve got deep experience in commercial real estate, skilled trades or technology, or you’re looking to apply your relevant experience to a new industry, join our team as we help shape a brighter way forward.

Job Summary

We are looking for a Mid-Level Machine Learning & Analytics Engineer to bridge the gap between predictive modeling, data architecture, and business strategy. In this role, you will design end-to-end data pipelines, build predictive and machine learning models, and create actionable analytics frameworks that drive core product decisions.

You will work closely with Data Engineers, Product Managers, and Business Stakeholders to transform complex structured and unstructured data into scalable, production-grade ML models and interactive analytical tools.

Key Responsibilities

• Predictive & ML Modeling: Build, evaluate, and fine-tune supervised/unsupervised machine learning models (e.g., churn prediction, lifetime value estimation, recommendation engines, forecasting).
• Advanced Analytics & Experimentation: Design and execute A/B testing, statistical hypothesis testing, and causal inference models to evaluate product features and strategic business initiatives.
• Data Engineering & Pipeline Development: Construct, maintain, and optimize ETL/ELT pipelines using SQL and Python to clean, transform, and aggregate raw data into feature‑ready datasets.
• Analytics Engineering & Business Intelligence: Model data for downstream analytics using frameworks like dbt, building high‑performing data schemas (star/snowflake) and self‑service BI dashboards (e.g., Tableau, Looker, Power BI).
• Model Deployment & Monitoring: Wrap predictive models into lightweight APIs (e.g., FastAPI) for batch or real‑time inferences, setting up automated alerts for data drift, performance degradation, and pipeline failures.
• Cross‑Functional Translation: Communicate technical methodologies, model trade‑offs, and analytics insights clearly to non‑technical business leaders and stakeholders.

Required Qualification"s

• 3-5 years of hands‑on experience in machine learning, analytics engineering, or data science showing clear professional advancement
• Bachelor’s degree or Advanced Students in Computer Science, Statistics, Data Science, Mathematics, Engineering, Economics, or relevant field.
• Outstanding oral and written English communication skills, demonstrating a track record of articulating intricate engineering details into actionable business perspectives for diverse audiences.
• Practical, proven experience operationalizing and deploying ML systems within live production environments rather than relying strictly on interactive notebooks.
• Demonstrated experience managing feature extraction, hyperparameter optimization, model validation, and cross‑validation techniques.
• Solid familiarity with dimensional schema architecture and modern enterprise data strategies.

Location:

On‑site –Belen, CRI

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