IN_Associate_Data Science & Engineering_Data & Analytics_Advisory_Kolkata

Technical duties

• Design and build analytical, deep learning, and machine learning algorithms using Python and R.
• Develop scalable data pipelines and cloud-based solutions leveraging AWS services.
• Implement statistical and data mining techniques including Linear & Logistic Regression, Time Series analysis, and Non-parametric methods.
• Integrate Generative AI platforms such as OpenAI and Google Gemini within data engineering pipelines.
• Write complex SQL queries for data extraction, manipulation, and database management.

Project responsibilities

• Building scalable data architectures and cloud-based pipelines on AWS to ingest high-volume client datasets.
• Developing predictive modeling solutions utilizing machine learning algorithms such as decision trees, bagging, and boosting.
• Deploying production-ready Generative AI applications integrated with cloud services and APIs like OpenAI and Google Gemini.

A typical day (Karyvio’s outline for this kind of role — not an employer commitment)

• Participate in daily stand-ups and sprint planning sessions with data engineering and analytics teams.
• Write, test, and debug Python code for data ingestion and transformation pipelines.
• Collaborate with senior consultants to analyze client data requirements and design algorithm specifications.
• Perform code reviews, document data pipelines, and maintain version control repositories.
• Explore emerging Generative AI tools and evaluate their applicability to client business problems.

Skills that matter, and why

• Python

  Core programming language required for building machine learning algorithms, scripting data pipelines, and integrating Generative AI APIs like OpenAI and Google Gemini.

  How to learn it: Practice data manipulation with Pandas and NumPy, build REST APIs using Flask or FastAPI, and implement object-oriented programming principles.

• AWS

  Cloud platform used to host scalable data pipelines, manage cloud-based data engineering tools, and deploy production-grade AI/ML workflows.

  How to learn it: Gain hands-on experience with core AWS services such as S3, EC2, IAM, and AWS Lambda through cloud certification labs or personal projects.

• Data Engineering

  Essential for designing and developing scalable data pipelines that clean, transform, and transport large datasets into analytical models.

  How to learn it: Study ETL pipeline design patterns, master SQL for data querying, and learn distributed data processing frameworks like Apache Spark.

• Generative AI

  Enables the design, development, and deployment of modern GenAI applications in production environments using platforms like OpenAI and Google Gemini.

  How to learn it: Experiment with LLM prompt engineering, fine-tuning pre-trained models via APIs, and building retrieval-augmented generation (RAG) applications.

• SQL

  Fundamental for querying relational databases, extracting raw data for feature engineering, and validating metrics within data pipelines.

  How to learn it: Practice complex SQL queries including window functions, joins, subqueries, and database indexing on platforms like LeetCode or HackerRank.

• Statistical Analysis

  Provides the mathematical foundation for regression, decision trees, time series analysis, and validating predictive modeling results.

  How to learn it: Revise probability distributions, hypothesis testing, and statistical machine learning concepts using online statistics courses.

Preparation plan

1. Strengthen your programming proficiency in Python, R, and SQL for data manipulation and querying.
2. Review statistical and data mining techniques, including linear regression, logistic regression, decision trees, and boosting methods.
3. Gain hands-on experience with AWS core services utilized for data storage, processing, and pipeline orchestration.
4. Explore Generative AI frameworks and integrate APIs from services like OpenAI or Google Gemini into mini applications.
5. Practice building end-to-end data pipelines, taking raw data from a source, transforming it, and loading it into a cloud target.

Project ideas to show you can do the job

Interview guidance

• Brush up on core Python data structures, algorithms, and libraries used for data analysis and modeling.
• Revise statistical concepts and data mining algorithms such as decision trees, regression, and time series analysis.
• Understand cloud data engineering patterns, specifically how to build and scale data pipelines on AWS.
• Be ready to discuss practical implementations and use cases for Generative AI tools like OpenAI and Google Gemini.
• Prepare to walk through past academic or internship projects focusing on how you handled data cleaning, modeling, and deployment.