Data Engineer / Data Scientist – AI/ML

About the role

CloudNexus is seeking a versatile Data Engineer / Data Scientist with AI/ML expertise to design and maintain data pipelines, develop predictive models, and deliver production‑ready machine‑learning solutions. The role supports a variety of projects, adapting to shifting requirements and timelines.

Key responsibilities

• Build, optimize, and monitor data pipelines using Python, PySpark, SQL, Databricks and Apache Spark.
• Develop, evaluate, and deploy machine‑learning models for use‑cases such as churn prediction and lift analysis.
• Perform feature engineering, experiment tracking, and model monitoring with MLflow and related MLOps tools.
• Collaborate with DevOps teams using Git, Azure DevOps, YAML and Databricks Asset Bundles to ensure reliable CI/CD for data and model assets.
• Conduct data analysis, predictive analytics, and experiment design to support business decisions.

Required profile

• 1–3 years of hands‑on experience in data engineering, data science or AI/ML.
• Strong programming skills in Python, PySpark and SQL.
• Practical experience with Databricks, Apache Spark and related data platforms.
• Good understanding of statistical concepts, predictive modeling and feature engineering.
• Exposure to MLOps practices such as MLflow, model monitoring and production workflows is a plus.
• Excellent analytical, problem‑solving and communication abilities.

Required skills

• Python
• PySpark
• SQL
• Databricks
• Apache Spark
• Unity Catalog
• ETL/ELT
• Machine Learning
• Predictive Modeling
• Feature Engineering
• Model Evaluation
• Churn Prediction
• SHAP
• Lift Analysis
• Experimentation
• MLflow
• Model Monitoring
• Feature Pipelines
• Git
• Azure DevOps
• YAML
• Databricks Asset Bundles