Stephenson Ebinezer
Studied at Birla Institute of Technology and Science
Works at Verizon
Available tomorrow at 3:30 AM UTC
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About Stephenson
I've spent 12+ years building a career in data science and AI — starting as an analyst and working my way up to a senior technical leadership role, with a move to a global architecture role at a Fortune 500 company on the horizon. Along the way, I completed my B.E., an MBA, and an M.Tech in AI & ML, because I've always believed in pairing hands-on experience with strong fundamentals. What I care most about as a coach is intuition — helping people actually understand the math and logic behind AI, not just memorize steps. I've spent the last 3 years mentoring 5,000+ learners through a complete data science curriculum, and I especially enjoy working with people from non-technical backgrounds who assume AI is "not for them." It usually just needs to be explained the right way.
Why do I coach?
I coach because I remember what it felt like to be intimidated by the math and jargon in AI early in my career. Once the fundamentals clicked for me, everything else became easier — and I want to give other people that same "it finally makes sense" moment, especially those without a traditional tech background who assume this field isn't accessible to them.
Work Experience
Senior Manager, Data Science
Verizon
April 2024 - Present
Agentic AI Architecture: Designed and deployed a production multi-agent order monitoring system using LangGraph — comprising specialized agents for anomaly detection, escalation routing, and self-healing workflow orchestration across large-scale enterprise operations. LLM Observability & Evaluation: Integrated LangSmith for end-to-end agent traceability, prompt versioning, and evaluation — ensuring production LLM pipelines are interpretable, debuggable, and continuously improving. RAG & Knowledge Grounding: Engineered Retrieval-Augmented Generation (RAG) and Graph RAG pipelines to ground LLM outputs in live enterprise data, significantly reducing hallucination risk and improving decision accuracy for operational intelligence use cases. LLM Fine-Tuning: Led domain-specific LLM fine-tuning initiatives using instruction tuning and parameter-efficient methods (PEFT/LoRA) to adapt foundation models to Verizon's operational and financial data contexts. AIOps & Intelligent Automation: Architected AIOps solutions that automated cross-functional workflows, standardized anomaly response playbooks, and reduced operational variance — delivering measurable improvements to system stability and P&L outcomes. FP&A & Financial Intelligence: Led Financial Planning & Analysis initiatives using advanced ML forecasting models, enabling strategic risk mitigation and data-driven performance optimization across large-scale business operations. Team Leadership & Thought Leadership: Mentored and scaled a high-performance AI/ML engineering team; owned architecture decisions, roadmap prioritization, and executive stakeholder alignment. Enabled 5,000+ learners externally via GUVI and delivered 15+ guest lectures at premier institutions including VIT. Financial Forecasting & FP&A, Anomaly Detection & Risk Mitigation and +6 skills
Data Science & AI Mentor
GUVI Geek Networks
June 2023 - Present
Mentored 5,000+ learners through a complete data science and AI curriculum, covering statistics, machine learning fundamentals, and applied GenAI/LLM concepts. Delivered structured lessons, hands-on project reviews, and 1:1 career guidance to help learners transition into data science and AI roles. Invited guest lecturer at premier institutions, including VIT, on applied data science and AI topics.
Data Science Consultant
Verizon
July 2022 - April 2024
Early LLM & GenAI Adoption: Prototyped and evaluated early LLM-based pipelines for internal knowledge retrieval and customer interaction summarization — laying the foundation for the production agentic systems built in the Senior Manager role. RAG Prototyping: Designed initial Retrieval-Augmented Generation (RAG) proof-of-concepts to surface contextual answers from unstructured enterprise documents, reducing analyst lookup time significantly. ML-Powered Customer Analytics: Built and deployed ensemble ML models (Gradient Boosting, XGBoost) for customer churn prediction and retention scoring, improving engagement KPIs through personalized intervention workflows. Recommendation Engines: Developed collaborative and content-based filtering recommendation systems to boost conversion rates and customer lifetime value across digital touchpoints. Automated Reporting Pipelines: Replaced manual reporting workflows with automated ML-driven dashboards, reducing reporting cycle time and enabling near-real-time KPI visibility for business stakeholders. Predictive Analytics: Delivered end-to-end predictive models for operational decision-making, combining classical ML approaches with early generative AI tooling to produce richer, more actionable insights. Web Analytics, PyTorch and +14 skills
Data Analytics Specialist
Verizon
October 2019 - June 2022
NLP & Topic Modeling: Developed an NLP-based Latent Dirichlet Allocation (LDA) topic modeling system on customer call transcripts, identifying top call drivers and reducing IVR call volume by 4% — translating directly to cost savings at scale. Predictive ML — Customer Retention: Built a Random Forest win-back classification model achieving 85% accuracy, enabling proactive outreach to at-risk customers and measurably improving retention rates. 5G & LTE Analytics Dashboards: Designed and maintained Adobe Analytics dashboards tracking 5G Home Internet and LTE performance KPIs, giving operational teams real-time visibility into network quality and customer experience signals. Feature Engineering & Model Optimization: Applied rigorous feature selection, cross-validation, and hyperparameter tuning across classification and regression models — ensuring production models remained accurate and interpretable under evolving data distributions. Statistical Analysis & Hypothesis Testing: Conducted A/B testing and statistical significance analyses to validate product and operational decisions, reducing reliance on intuition-based choices across cross-functional teams. Data-Driven Decision Support: Synthesized complex, multi-source datasets into structured insights and executive-ready reporting, enabling faster and more confident strategic decisions across product and operations leadership. Web Analytics, Data Science and +9 skills
Analytics & AI Consultant
HCL Technologies
June 2018 - October 2019
Early Data Science Foundations: Transitioned from business analytics into applied data science — building first hands-on experience with statistical modeling, predictive analytics, and ML-driven decision systems in a large enterprise consulting environment. SQL-Driven KPI Automation: Designed and automated SQL-based reporting pipelines for tracking business KPIs across client accounts — replacing manual workflows and enabling consistent, timely performance monitoring at scale. Statistical Modeling & Hypothesis Testing: Applied regression analysis, hypothesis testing, and statistical significance frameworks to uncover trends, validate business assumptions, and identify process improvement opportunities across client datasets. A/B Testing & Funnel Optimization: Designed and analyzed A/B experiments across digital funnels, identifying conversion bottlenecks and implementing data-backed fixes that improved conversion rates by 10%. Project Estimation with PERT Analysis: Applied Program Evaluation and Review Technique (PERT) to improve project time estimation accuracy by 15% — an early application of probabilistic modeling to operational planning. Cross-functional Data Storytelling: Synthesized analytical findings into structured business narratives for client stakeholders — developing the communication and translation skills that now underpin executive-level AI strategy presentations. SQL, Statistical Data Analysis and +2 skills
Business Analyst
Acentra Health
July 2015 - May 2018
Leveraged SQL extensively for data extraction, analysis, and reporting—transforming raw data into actionable business insights. Acted as a bridge between product teams, stakeholders, and developers, ensuring seamless translation of business needs into technical requirements. Collaborated with cross-functional teams to analyze processes, identify gaps, and recommend data-driven solutions to improve efficiency and product performance. Supported decision-making by creating data summaries, dashboards, and trend analyses for key business metrics. Contributed to requirement gathering, documentation, and validation, ensuring business objectives were clearly understood and implemented accurately. Business Insights, SQL and +1 skill
Product Analyst
Bioscreen Instruments Private Limited
July 2012 - July 2013
Conducted market and competitor analysis to uncover trends, customer needs, and product opportunities that guided strategic decisions. Collaborated with product and marketing teams to translate research insights into actionable recommendations, strengthening data-driven product development.
Stephenson was also given offers to work at
Caterpillar
Education
Birla Institute of Technology and Science
Master of Technology - MTech, Artificial Intelligence and Machine Learning
2022 - 2024
Sri Ramachardra Medical College and Research Institute
Master of Business Administration (MBA), Information Technology, Health Systems Management
2013 - 2015
Anna University
Bachelor of Engineering
2009 - 2012