Data Scientist

About the Role

At Deloitte Consulting LLP, Data Scientists and ML Engineers leverage advanced analytics and machine learning to transform data into actionable business insights and innovative solutions. This is a Consultant-level role.

Key Responsibilities

• Build & Deploy ML Solutions
• Lead the design and deployment of advanced, scalable ML solutions for enterprise use cases.
• Oversee data ingestion, wrangling, and processing at scale using appropriate big data tools.
• Architect, develop, and optimize APIs for robust ML model serving in production environments.
• MLOps & Production Engineering
• Utilize MLflow for experiment tracking, reproducibility, and lifecycle management.
• Drive cloud-native deployment, monitoring, and automation for ML solutions.
• Communicate & Grow
• Mentor consultants, guide technical reviews, and promote best practices in software engineering and MLOps.
• Communicate complex insights to stakeholders and ensure alignment with client objectives.
• Keep current with AI/ML advancements and lead knowledge-sharing within project teams.

Requirements & Tech Stack

• Experience
• 2-5 years of experience developing, deploying, and interpreting machine learning solutions.
• Demonstrated experience with core statistical and ML algorithms, including linear regression, logistic regression, decision trees, clustering, dimensionality reduction (e.g., PCA), and time series analysis and forecasting.
• Hands-on experience using Python for data preparation, wrangling (cleaning, feature engineering), and model development.
• Proven ability to work with large datasets using Pandas, NumPy, SQL, or PySpark.
• Experience implementing models with deep learning frameworks (e.g., TensorFlow, PyTorch, or Keras).
• Solid experience in data visualization using Python libraries (Matplotlib, Seaborn) or business visualization platforms (Tableau, Power BI).
• Practical experience creating and serving Python-based APIs using frameworks like FastAPI or Flask.
• Ability to code, evaluate, and deploy basic ML models within data pipelines.
• Experience working on projects within cloud environments (Azure, AWS, or GCP) is preferred.
• Strong analytical, teamwork, and communication skills.
• Education
• B.E./B.Tech./M.C.A./M.Sc./Ph.D. in Computer Science, Data Science, Engineering, Statistics, Mathematics, or a related discipline.
• Preferred Skills
• Experience with large-scale data processing platforms (PySpark, Dask, BigQuery, Databricks).
• Advanced cloud-based data tool knowledge and ML deployment skills.
• Familiarity with productivity tools like GitHub Copilot or AWS CodeWhisperer.
• Understanding of industry trends in ML, AI, and MLOps.

Core Competencies

• Strong ML engineering and experimentation craft (Python, MLflow).
• Ability to translate ML solutions into production-ready APIs.
• Cloud-native deployment and automation mindset.
• Clear communicator of complex analytical insights.
• Mentor and promoter of engineering/MLOps best practices.
• Curious and current on AI/ML advancements.