Jr. Data Scientist

We at Mile are seeking a proactive and detail-orientedJunior Data Scientist / MLOps Associateto join our data team. This entry-level role is ideal forrecent graduates or professionals withup to two years of experiencewho are passionate about bridging the gap between machine learning development and production deployment. You will support the development, analysis, deployment, and maintenance of machine learning models in real-world environments.

Responsibilities

Assist in collecting, cleaning, and preprocessing structured and unstructured datasets for model training and evaluation.

Perform exploratory data analysis (EDA) to uncover patterns, trends, and potential data issues.

Support the development and implementation of machine learning models under the guidance of senior team members.

Run experiments to evaluate model performance, document results, and iterate on improvements.

Create clear and effective visualizations to communicate insights and model results to technical and non-technical stakeholders.

Contribute to documentation of workflows, methodologies, and deployment procedures to ensure reproducibility and operational stability.

Participate in the deployment, versioning, and basic monitoring of models in production environments.

Continuously learn and stay updated with developments in data science, machine learning, and MLOps practices.

Qualifications

Bachelor's degree in Computer Science, Data Science, or a related field.

At least 2 years of experience in data science, machine learning, or a related domain.

Proficiency in Python and core data science libraries (e.g., NumPy, Pandas, Scikit-learn).

Basic understanding of statistical concepts, probability, and machine learning algorithms.

Knowledge of data preprocessing, feature engineering, and model evaluation techniques.

Experience with data visualization libraries such as Matplotlib and Seaborn.

Familiarity with SQL and database fundamentals.

Strong analytical and problem-solving skills.

Exposure to model deployment and automation via competitions or personal projects is a plus.

Familiarity with cloud platforms (AWS, GCP, or Azure) and their ML services.

Technical Skills

Programming: Python (required)

Libraries/Frameworks: NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn

Machine Learning: Basic understanding of supervised/unsupervised learning, regression, classification, clustering, evaluation

Data Manipulation: Cleaning, preprocessing, feature engineering

Databases: Basic SQL

Version Control: Git

MLOps/DevOps: Basic understanding of CI/CD, model versioning, and monitoring. Familiarity with Docker and Kubernetes is a plus.

Soft Skills

Strong communication skills with the ability to explain technical concepts clearly

Keen interest in the operational aspects of machine learning

High attention to detail and strong organizational abilities

Team-oriented with collaborative work ethic

Proactive mindset with a drive to improve workflows

Willingness to learn emerging technologies and MLOps tools