Data Scientist

Role Overview

Develop statistical and machine learning models to solve business, product, and operational problems. You will collect, clean, transform, and analyze datasets, design experiments, and build scalable data and ML workflows using cloud platforms. The role involves productionizing models, applying MLOps practices, and exploring modern AI techniques like LLMs and Generative AI.

Responsibilities

• Develop statistical and machine learning models to solve business, product, and operational problems.
• Collect, clean, transform, analyze, and validate structured and unstructured datasets.
• Build scalable data and ML workflows using cloud platforms and managed machine learning services.
• Work with big data technologies such as Spark, Hadoop, and Databricks.
• Productionize machine learning models and contribute to reliable ML pipelines, deployment workflows, and monitoring.
• Apply MLOps practices including model versioning, experiment tracking, and CI/CD for ML.
• Create meaningful dashboards, reports, and visualizations.
• Explore and contribute to applications involving LLMs, generative AI, and NLP.
• Investigate model drift, data-quality issues, and performance degradation.

Requirements

• Minimum 2 years of professional experience in Data Science, Machine Learning, Analytics, or AI.
• Strong understanding of supervised and unsupervised machine learning and statistical concepts.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Experience with big data technologies such as Apache Spark, Hadoop, or Databricks.
• Exposure to MLOps practices and tools like MLflow.
• Experience with data visualization using Tableau, Power BI, or Python libraries.
• Exposure to LLMs, generative AI, or NLP.
• Strong problem-solving, analytical, and communication skills.

Skills

• Python
• SQL
• Apache Spark
• AWS
• MLflow

Benefits

• Health insurance