Data Science Engineering Consultant

HypTechie; Bengaluru; Hybrid; INR 500,000 - 2,500,000; Full time

Job summary

HypTechie in Bengaluru offers a hybrid role for a Data Science Engineering Consultant to lead end-to-end data projects, with a strong foundation in Python, AI/ML, and data engineering.

You will build scalable data apps, design data models, validate models, and collaborate with global teams while applying best practices in governance and security.

Qualifications

• Bachelor’s degree in CS or related field.
• 5–8 years in data science and software development.
• Proficiency in Python, SQL, AI/ML, and scripting.
• Hands-on experience with GenAI/LLM models (e.g., GPT, LLaMA, Claude).
• Strong Python libraries: LangChain, HayStack, Transformers.
• ML frameworks: Scikit-learn, XGBoost, Random Forests, Pandas.
• EDA, feature engineering, and model validation.
• Experience with Cloud Data Lake tools like Snowflake.
• Completed at least one full-cycle data science project.
• Statistical modeling understanding.
• Basic data viz (Tableau) integration.

Responsibilities

• Lead the development of scalable data applications with focus on correctness, resilience, and quality.
• Troubleshoot data pipelines and assess impact analyses.
• Own the software development lifecycle from requirements to deployment.
• Create technical specs, regression tests, and test reports.
• Design data models to support analytics, reporting, ML/AI apps.
• Collaborate with global teams and communicate effectively.
• Provide thought leadership in data modeling, warehousing, governance, security.

Skills

Python

SQL

AI/ML

Data engineering

GenAI/LLM

LangChain

HayStack

Transformers

Scikit-learn

XGBoost

Pandas

EDA

Feature engineering

Model validation

Snowflake

Education

Bachelor’s degree in Computer Science or related field

Tools

Snowflake

Tableau

Job description

Location: Hybrid (Office + Home) Job Type: Permanent Experience: 5 – 8 Years Salary Range: ₹5,00,000 – ₹25,00,000 per annum

Our client is looking for a skilled Data Science Engineering Consultant with a strong foundation in Python, AI/ML, and data engineering to lead and execute impactful end-to-end data science projects. The role requires hands‑on experience with machine learning, LLMs/GenAI, data modeling, and cloud technologies.

Key Responsibilities:

• Lead the development of scalable data applications with focus on correctness, resilience, and quality.


• Troubleshoot application and data pipeline issues, conducting impact analysis and resolving efficiently.


• Take ownership of the software development lifecycle—from requirements through design, implementation, and deployment.


• Create technical specifications, regression testing documents, and detailed test case reports.


• Design and implement data models to support analytics, reporting, and ML/AI applications.


• Collaborate and communicate effectively with global teams.


• Provide thought leadership in Data Modeling, Warehousing, Governance, Security, and related best practices.

Mandatory Requirements:

• Bachelor’s degree in Computer Science or related field.


• 5–8 years of relevant experience in data science and software development.


• Proficient in Python, SQL, AI/ML, and scripting languages.


• Hands‑on experience developing and tuning GenAI/LLM models (e.g., GPT, LLaMA, Claude).


• Strong experience with Python libraries such as LangChain, HayStack, and Transformers.


• Practical knowledge of ML frameworks like Scikit-learn, XGBoost, Random Forests, and Pandas.


• Experience with Exploratory Data Analysis (EDA), feature engineering, and model validation.


• Experience with Cloud Data Lake tools like Snowflake.


• Experience completing at least one full-cycle data science project.


• Solid understanding of statistical modeling and analysis techniques.


• Basic understanding of data visualization (e.g., Tableau) to integrate deployed models.

Desirable Requirements:

• AI/ML certification is a plus.


• Familiarity with ETL tools (Informatica).


• Experience in Tableau installation, configuration, and upgrades.


• Working knowledge of collaboration/documentation tools (Confluence, SharePoint, JIRA).


• Exposure to GitLab CI/CD pipelines.


• Comfort with Agile methodologies and full SDLC.


• Strong communication, collaboration, and problem‑solving skills.


• Proactive mindset with flexibility to work independently or across multiple initiatives.