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About This Role
Senior Data Scientist Lead \- R01566194
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About Brillio:
Brillio is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. Brillio, renowned for its world\-class professionals, referred to as "Brillians", distinguishes itself through their capacity to seamlessly integrate cutting\-edge digital and design thinking skills with an unwavering dedication to client satisfaction.
Brillio takes pride in its status as an employer of choice, consistently attracting the most exceptional and talented individuals due to its unwavering emphasis on contemporary, groundbreaking technologies, and exclusive digital projects. Brillio's relentless commitment to providing an exceptional experience to its Brillians and nurturing their full potential consistently garners them the Great Place to Work® certification year after year. Senior Data Scientist###### Job requirements
A highly experienced Senior Data Science Lead to drive advanced AI initiatives with a strong focus on Computer Vision (CV) and Optical Character Recognition (OCR). This role will provide technical leadership, architecture direction, and hands\-on expertise in designing, developing, and deploying scalable AI solutions for enterprise\-grade use cases.
This is a highly client\-facing role, requiring a strong blend of technical depth, consultative mindset, and leadership capability. The ideal candidate will combine deep technical expertise with strong stakeholder management and team leadership capabilities.
Key Responsibilities
Technical Leadership
- Lead the design, development, and deployment of end\-to\-end AI/ML solutions, with primary focus on Computer Vision and OCR use cases.
- Architect scalable and production\-ready AI systems, ensuring performance, accuracy, security, and maintainability.
- Drive adoption of state\-of\-the\-art AI techniques including deep learning, transformers, multimodal models, and document intelligence.
- Review and guide model selection, feature engineering, training strategies, and evaluation metrics.
Computer Vision \& OCR Expertise
- Lead CV initiatives such as image classification, object detection, segmentation, face/text recognition, and video analytics.
- Design and optimize OCR pipelines for structured and unstructured documents (invoices, forms, IDs, contracts, handwritten text).
- Implement and enhance document understanding workflows including layout analysis, key\-value extraction, and semantic interpretation.
- Improve model accuracy through data augmentation, active learning, and continuous feedback loops.
AI Engineering \& Deployment
- Work closely with engineering teams to deploy models using MLOps best practices.
- Design CI/CD pipelines for ML, model monitoring, drift detection, and automated retraining.
- Ensure AI solutions are cloud\-native and scalable (AWS / Azure / GCP).
Stakeholder \& Delivery Management
- Partner with business stakeholders to translate business problems into AI\-driven solutions.
- Provide technical thought leadership during client discussions, solutioning, and proposals.
- Own delivery outcomes, timelines, and quality for AI programs.
People \& Practice Leadership
- Mentor and guide data scientists, ML engineers, and analysts.
- Establish best practices, coding standards, and governance for AI development.
- Contribute to building AI capability, accelerators, and reusable frameworks within the organization.
Education
- Bachelor’s or master’s degree in computer science, Data Science, Artificial Intelligence.
Experience
- 10–15\+ years of experience in Data Science / AI, with at least 5\+ years in a leadership role.
- Proven, hands\-on experience delivering Computer Vision and OCR solutions at scale.
- Strong experience working in enterprise or consulting environments.
Soft Skills \& Leadership Traits
- Strategic thinker with strong problem\-solving skills.
- Ability to balance hands\-on technical work with leadership responsibilities.
- Excellent communication, influencing, and stakeholder management skills.
- Strong mentoring mindset and passion for building high\-performing teams.
Know more about DAE: https://www.brillio.com/services\-data\-analytics/
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Equal Employment Opportunity Declaration
Brillio is an equal opportunity employer to all, regardless of age, ancestry, colour, disability (mental and physical), exercising the right to family care and medical leave, gender, gender expression, gender identity, genetic information, marital status, medical condition, military or veteran status, national origin, political affiliation, race, religious creed, sex (includes pregnancy, childbirth, breastfeeding, and related medical conditions), and sexual orientation.
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Salary: 125,000\-130,000 USD per year salary
Salary Context
This $125K-$130K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Brillio LLC, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($127K) sits 36% below the category median. Disclosed range: $125K to $130K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Brillio LLC AI Hiring
Brillio LLC has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Product Manager. Positions span New York, NY, US, Santa Ana, CA, US, Jersey City, NJ, US. Compensation range: $130K - $180K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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