ML Engineer

About the role

Company Background

Coherent Solutions is a global digital product engineering company founded in 1995 and headquartered in Minneapolis, Minnesota. With a presence in nine countries and a team of more than 1,700 engineers, the company provides custom software development and engineering services across web and mobile applications, cloud, DevOps, and data. Coherent Solutions works with clients worldwide to build and scale digital products while fostering a culture of collaboration, transparency, and continuous professional growth.

Project Description

You will work with senior engineers on internal classical machine learning initiatives. The role combines structured learning, practical delivery, and gradual ownership of increasingly complex tasks.

Technologies

- Python, pandas, NumPy, scikit-learn, Jupyter

- SQL

- XGBoost, LightGBM

- MLflow

- FastAPI

- Git, Docker, pytest

What You'll Do

- Prepare and validate structured datasets for machine learning tasks, including cleaning, transformation, encoding, scaling, and missing-value handling;

- Conduct exploratory data analysis to identify patterns, data quality issues, and opportunities for modeling;

- Build, test, and compare ML models for tasks such as regression, classification, clustering, dimensionality reduction, and anomaly detection;

- Develop and select features with guidance from senior engineers;

- Run experiments, tune model parameters, and evaluate results using appropriate validation approaches and metrics;

- Help maintain reproducible ML pipelines, notebooks, experiment tracking, and technical documentation;

- Write and maintain automated tests for data and model-related code;

- Support the delivery of models through REST APIs or batch-processing workflows;

- Document model assumptions, results, limitations, and technical decisions;

- Work closely with senior engineers, incorporate feedback, and gradually take ownership of more complex tasks;

Job Requirements

- 1+ year of hands-on Python development experience, including experience with pandas, NumPy, scikit-learn, and Jupyter;

- A strong foundation in statistics, probability, and linear algebra, plus exploratory data analysis skills;

- An understanding of supervised and unsupervised learning, and practical experience with regression, classification, clustering, dimensionality reduction, and anomaly detection;

- Experience preparing structured datasets, including cleaning, transformation, encoding, scaling, and missing-value handling, plus practical feature engineering and feature-selection skills;

- An understanding of train/validation/test splits, cross-validation, data leakage, overfitting, and regularization; the ability to select appropriate evaluation metrics, compare models, and perform error analysis;