Key Facts

Remote From:

Anywhere

Fixed term

English

Hard Skills

Other Skills

•
Forecasting
•
Analytical Thinking
•
Collaboration
•
Communication

Roles & Responsibilities

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)
Ability to translate complex datasets into clear, strategic recommendations

Requirements:

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

KIS Solutions

About KIS Solutions

Too often corporate IT teams and business counterparts are at odds. This is in large part because technologists too often focus on the technology solution itself rather than the underlying business problem. At KIS solutions we believe that technology is a tool to solve business problems. We focus on the business problem and operational processes which our solutions support. When building technology systems, the most challenging part of the development process is often deciding “what to build” not “how to build it”. It is sadly true however that many corporate IT teams spend so much their time focusing on the “how” rather than the “what”. Once you have answered the challenging questions around “what to build”, the rest falls into place. Building the system should be simple! Founder Benjamin Quintana is an IT professional and entrepreneur with nearly 20 years of experience delivering impactful solutions across a broad range of industries. Ben has extensive experience with customer data analytics, data cleansing, customer identification, customer preference management, call center and logistical systems. He also has deep industry expertise in loyalty marketing applications and was directly responsible for the infrastructure of the loyalty program of a leading hotel group.

Founded: 2018

Company size: 51 - 200

LinkedIn See all jobs →

Job description

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