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Mid Machine Learning Engineer

Remote, USA Full-time Posted 2025-11-03

This is a remote position.

We are seeking a Machine Learning Engineer to join our team and work on a project centered on multi-year transactional data analysis and predictive modeling. This role offers the opportunity to directly impact client decision-making by uncovering hidden patterns in data, generating forecasts, and deploying models that drive measurable business outcomes.

You will work with a consultancy with over 20 years of experience helping organizations transform challenges into growth opportunities, specialized in data-driven problem solving, identifying inefficiencies, and enabling businesses to scale through strategic insights and cost reduction initiatives.

Key Responsibilities

  • Receive, clean, and preprocess large volumes of transactional and financial data

  • Design, train, and optimize predictive models to identify trends, clusters, and anomalies

  • Generate forecasts and actionable insights to support business strategies

  • Collaborate with data engineers and consultants to integrate models into production systems

  • Contribute to hypothesis-driven problem solving and help shape data-backed strategies for clients



Requirements

  • Strong proficiency in Python, with libraries such as Pandas, NumPy, and Scikit-learn

  • Hands-on experience with machine learning models (e.g., XGBoost, Random Forest)

  • Knowledge of model interpretability and explainability techniques (decision trees, SHAP, or LLM-based explanations)

  • Analytical mindset with a solid business-oriented problem-solving approach

  • Ability to translate complex datasets into clear, strategic recommendations

  • Intermediate English

Desirables

  • Experience with LLMs (Large Language Models) and workflow automation tools such as n8n

  • Familiarity with Java and Node.js for building and maintaining data pipelines

  • Experience with data extraction, API integrations, and implementing return triggers to source systems


Originally posted on Himalayas

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