The Role
Translates business challenges into data science problems, develops and validates statistical models and OKRs, wrangles complex datasets, and delivers insights through data storytelling. Leads modeling decisions, feature engineering, machine learning evaluation, workflow optimization, and scalable solution implementation. Collaborates with engineering and product teams, promotes data literacy, and applies cloud, big data, MLOps, and potentially deep learning and agentic AI technologies.
Summary Generated by Built In
Senior Data Scientist
Job requirements
- Translate complex business challenges into structured data science problems, ensuring alignment with organizational objectives and measurable outcomes
- Develop, monitor, and validate OKRs using advanced statistical techniques to deliver actionable insights and track progress
- Execute advanced data wrangling, cleansing, and transformation on large, complex datasets to enable robust modeling and analysis
- Deliver impactful data-driven insights through clear data storytelling, utilizing visualization tools to communicate findings effectively to stakeholders
- Apply design thinking methodologies to create innovative analytical solutions and continuously optimize data science workflows and processes
- Lead technical decision-making for modeling iterations, optimizing model performance, and balancing computational efficiency with business requirements
- Collaborate with cross-functional teams, including engineering and product, to implement scalable data science solutions that drive business value
- Promote data literacy and foster a culture of data-driven decision-making by sharing best practices and industry trends across the organization
- 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
- 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
- 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
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:
Required Skills:
Preferred Skills:
Desired Qualifications:
Skills Required
- 5–8 years of experience, including at least 5 years in data science, analytics, or related fields
- Recent exposure to agentic AI solutions
- Advanced proficiency in Python or R for data wrangling, preprocessing, and statistical analysis
- Expertise in statistical modeling and performance metric validation
- Experience with 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 Spark or Hadoop
- Experience with AWS, Azure, or Google Cloud data platforms
- Knowledge of MLOps practices and deployment pipelines
- Bachelor’s degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline
- Experience with TensorFlow or PyTorch
- Exposure to agentic AI and rapid domain adaptation
- Experience with automation tools and scripting for data workflows
- Certification in Data Science or Machine Learning from a recognized institution
- Relevant coursework or certification in statistical analysis or business analytics
Brillio Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Brillio and has not been reviewed or approved by Brillio.
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Healthcare Strength — Healthcare is considered comprehensive, including medical coverage for employees and dependents alongside life, disability, and accidental death protections. Feedback suggests these protections are a core strength of the package.
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Leave & Time Off Breadth — Time-off options include paid leave and parental leave, with flexible or ‘flexible PTO’ approaches cited in some contexts. Feedback suggests this breadth helps support work-life balance when team norms permit usage.
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Wellbeing & Lifestyle Benefits — Wellbeing offerings span counseling, financial-management sessions, fitness programs, and travel insurance, plus region-specific extras like discounted IT hardware and work-from-home essentials. Feedback suggests these add-ons enhance perceived value beyond core insurance.
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The Company
What We Do
Brillio is the leader in global digital business transformation, applying technology with a human touch. We help businesses define internal and external transformation objectives, and translate those objectives into actionable market strategies using proprietary technologies. With 2600+ experts and 13 offices worldwide, Brillio is the ideal partner for enterprises that want to quickly increase their core business productivity, and achieve a competitive edge, with the latest digital solutions.
