Principal Data Scientist- Architect

2 weeks ago

Pune, Maharashtra, India Neurealm Full time US$ 1,20,000 - US$ 2,00,000 per year

Pune, Maharashtra, India

Department

AI/ML

Job posted on

Jul 21, 2025

Employment type

Full Time Employee

A Principal Data Scientist job focuses on leading the development and application of machine learning models and data-driven solutions, often within a team or organization. This role involves both technical expertise in data science and leadership skills to guide other data scientists and collaborate with stakeholders.

Here's a more detailed breakdown of the responsibilities:

Core Responsibilities:

Lead Data Science Initiatives:

Drive the strategy and execution of data science projects, ensuring alignment with business goals.

Develop and Deploy Machine Learning Models:

Design, implement, and deploy machine learning models for various applications, including predictive modeling, classification, and recommendation systems.

Mentor and Guide Data Scientists:

Provide technical leadership and mentorship to junior data scientists, fostering their growth and development.

Collaborate with Stakeholders:

Work closely with product managers, engineers, and other teams to understand business needs and translate them into data-driven solutions.

Stay Up-to-Date with Emerging Technologies:

Continuously research and evaluate new machine learning techniques, tools, and platforms to improve the team's capabilities.

Communicate Findings:

Effectively communicate complex data insights and recommendations to both technical and non-technical audiences.

Technical Skills:

Advanced Machine Learning:

Expertise in various machine learning algorithms, including supervised, unsupervised, and reinforcement learning techniques.

Statistical Modeling:

Strong foundation in statistical modeling, hypothesis testing, and experimental design.

Programming Languages:

Proficiency in Python or other relevant languages for data analysis and model development.

Data Manipulation and Analysis:

Experience with large datasets, data cleaning, and feature engineering.

Cloud Computing:

Familiarity with cloud platforms like AWS and Azure

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