BR
BrillioData Science Lead - R01570348
LocationBangalore, Karnataka, India
Work modehybrid
Typefull-time
SeniorityLead
Experience5–8 yrs
DepartmentAI & Data Engineering, AI & Data Engineering : Data Science
Company size5,001–10,000 people
PostedSep 7, 2026 · 1mo ago
Verified live20h ago
At a glanceSummarised by Seekless from the posting.
5 - 8 years of experience, including at least 5 years of hands-on work in data science, analytics, or related fields, with recent exposure to agentic AI solutions
Advanced proficiency in Python or R for data wrangling, preprocessing, and statistical analysis
Expertise in statistical modeling and validation of performance metrics
Experience with data visualization tools such as Tableau, Power BI, or Matplotlib
Hands-on experience with machine learning algorithms and evaluation metrics
Strong background in feature engineering and data mining
Familiarity with big data technologies such as Spark or Hadoop
Experience with cloud-based data platforms including AWS, Azure, or Google Cloud
Knowledge of MLOps practices and deployment pipelines
Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline
Experience with deep learning frameworks such as TensorFlow or PyTorch
Exposure to agentic AI and rapid domain adaptation
Experience with automation tools and scripting for data workflows
Skills
Python
R
Tableau
Power BI
Matplotlib
Spark
Hadoop
AWS
Azure
Google Cloud
TensorFlow
PyTorch
Senior Data Scientist
Job requirements
Experience Range: 5 - 8 years of experience, including at least 5 years of hands-on work in data science, analytics, or related fields, with recent exposure to agentic AI solutions Key Responsibilities:
Responsibilities
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Translate complex business challenges into structured data science problems, ensuring alignment with organizational objectives and measurable outcomes
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Develop, monitor, and validate OKRs using advanced statistical techniques to deliver actionable insights and track progress
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Execute advanced data wrangling, cleansing, and transformation on large, complex datasets to enable robust modeling and analysis
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Deliver impactful data-driven insights through clear data storytelling, utilizing visualization tools to communicate findings effectively to stakeholders
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Apply design thinking methodologies to create innovative analytical solutions and continuously optimize data science workflows and processes
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Lead technical decision-making for modeling iterations, optimizing model performance, and balancing computational efficiency with business requirements
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Collaborate with cross-functional teams, including engineering and product, to implement scalable data science solutions that drive business value
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Promote data literacy and foster a culture of data-driven decision-making by sharing best practices and industry trends across the organization
Required Skills:
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Advanced proficiency in Python or R for data wrangling, preprocessing, and statistical analysis
•
Expertise in statistical modeling and validation of performance metrics
•
Experience with data visualization tools such as Tableau, Power BI, or Matplotlib
•
Hands-on experience with machine learning algorithms and evaluation metrics
•
Strong background in feature engineering and data mining
•
Familiarity with big data technologies such as Spark or Hadoop
•
Experience with cloud-based data platforms including AWS, Azure, or Google Cloud
•
Knowledge of MLOps practices and deployment pipelines
Preferred Skills:
•
Experience with deep learning frameworks such as TensorFlow or PyTorch
•
Exposure to agentic AI and rapid domain adaptation
•
Experience with automation tools and scripting for data workflows
Desired Qualifications:
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Bachelor’s degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline
•
Certification in Data Science or Machine Learning from a recognized institution, such as Microsoft Certified: Azure Data Scientist Associate or IBM Data Science Professional Certificate
•
Relevant coursework or certification in statistical analysis or business analytics