Azure Data Scientist

Unify Dots

1d•Remote

About The Position

UNIFY Dots is a global technology and software solutions company specializing in Microsoft Dynamics 365 based solutions. We are seeking an Azure Data Scientist who will be responsible for designing, building, and experimenting with machine learning models using Microsoft Azure.

Requirements

Bachelor’s Degree in Computer Science, Engineering, Information Technology, or related field
3–5 years of experience in data science, machine learning, or related roles
Strong experience with Python for data analysis and machine learning
Experience with Azure ML Python SDK (v2)
Hands-on experience with Azure Machine Learning (workspaces, experiments, models)
Proficiency using Jupyter Notebooks in Azure ML Studio for experimentation and modeling
Solid understanding of data modeling concepts (features, labels, training data, validation)
Experience building and comparing regression, classification, or clustering models
Ability to translate business requirements into machine learning solutions with clear outcomes
Strong analytical and problem-solving skills
Good communication skills in English (written and verbal)
Certified in Microsoft Azure AI Fundamentals (AI-900) and Microsoft Azure Data Fundamentals (DP-900)

Nice To Haves

Exposure to ML lifecycle practices (experimentation, evaluation, deployment readiness)
Familiarity with common ML libraries (e.g., scikit-learn, pandas, NumPy)
Experience working in cross-functional teams (business, engineering, analytics)

Responsibilities

Define business goals and translate them into machine learning problems and success metrics
Design and implement data models suited for analytical and machine learning workloads
Perform feature engineering and feature selection based on business context
Build, train, evaluate, and iterate machine learning models
Run experiments and track results using Azure Machine Learning
Use notebooks for data exploration, model development, and validation
Document assumptions, model behavior, and results for business and technical stakeholders
Collaborate with data engineers and solution architects to ensure data readiness
Work closely with business and technology teams to ensure models align with real business outcomes.