Machine Learning Engineer -Junior to Mid-Level (Contract)
Overview
We are seeking a Junior to Mid-Level Machine Learning Engineer / Data Scientist to support and enhance a portfolio of existing machine learning models at various stages of maturity. This individual will work closely with an established team to assist with model development, optimization, testing, and deployment efforts.
The ideal candidate will have 2–4 years of relevant professional experience with Python, machine learning, data engineering, and cloud-based data platforms. While the individual will contribute to model development and product ionization, they will receive guidance from the team and are not expected to independently own the entire MLOps lifecycle.
**This is a 3-month contract, with extension to 6 months based on performance.
Responsibilities
Perform data preparation, cleansing, transformation, and feature engineering activities.
Identify and develop meaningful features to improve model performance.
Support the development and enhancement of machine learning models, including multivariate regression, gradient boosting, and related predictive modeling techniques.
Assist with testing, validation, and performance evaluation of machine learning solutions.
Convert notebook-based work into reusable, modular Python functions and scripts suitable for production environments.
Support model deployment and ongoing optimization efforts.
Participate in daily stand-ups, team collaboration sessions, model reviews, demos, and documentation activities.
Contribute to continuous improvement initiatives and knowledge sharing across the team.
Required Skills & Experience
Approximately 2–4 years of relevant professional experience in Machine Learning, Data Science, Data Engineering, or a related field.
Strong proficiency in Python and the Pandas library.
Experience writing efficient SQL queries and working with large datasets.
Hands-on experience building, testing, and supporting machine learning models.
Experience with data preparation, feature engineering, and predictive analytics.
Familiarity with cloud environments such as AWS and Azure.
Experience working with Snowflake.
Understanding of software development best practices, testing, and deployment processes.
Ability to work in a collaborative, agile team environment.
Preferred Qualifications
Experience supporting production machine learning environments.
Familiarity with MLOps concepts and practices.
Experience transitioning analytical notebooks into production-ready Python code.
Exposure to model monitoring and ongoing model enhancement initiatives.
Technical Environment
Python
SQL
Pandas
AWS
Azure DevOps (ADO)
Snowflake
In-house MLOps framework
Benefits we offer
Access to e-Learning platforms.
Access to a virtual nutritionist
Amazing people-oriented organizational culture
Working from anywhere
Challenging projects using the latest technologies with clients from the US.