Driving the widespread use of data in decision-making

I have spent my career helping organizations leverage advanced analytics to improve decision-making in measurable ways. As a data science professional with 15+ years of practical experience, I deliver results at every stage of the analytic lifecycle – from data acquisition and preparation to the deployment of analytical results into decision-making.

​I have hands‑on experience with advanced techniques such as classification algorithms, ensemble models, clustering, regression analysis, advanced forecasting, market basket analysis, machine learning, artificial intelligence, and more. I have worked with multiple data, programming and analytical environments on premises or on cloud as well as hybrid models.

Beyond my technical abilities, I can bridge advanced analytics and business realities by working closely with business stakeholders, including executives. These characteristics make me uniquely qualified to help organizations accelerate and expand their application of analytics. 

My background

Achieving business transformation with predictive analytics

Projects

Check out some of my previous work

I have worked on numerous projects and in almost every industry and vertical throughout my career, and you can find a list below of the most interesting projects and those that have been published as Case Studies or otherwise made public.

This is by no means a complete nor exhaustive list of my work, but I hope that it allows for a sense of the type of work that I enjoy.

Professional experience

Senior Manager, Data Analytics & Insights

Arctera

December 2024 — March 2026

Arctera was split off from Veritas Technologies in December 2024 and then later acquired by Cloud Software Group in December 2025.

Tasked with increasing the use of data in the decision-making across the organization.

• Doubled sales forecast accuracy to within 5% of actual revenue by interviewing sales leaders, defining pipeline health indicators, and deploying machine learning models that predicted quarterly revenue based on week-by-week sales team performance.

• Reduced manual reporting from several hours per week to under 5 minutes by automating weekly sales pipeline health assessments and creating a scalable model-driven process for frequent executive updates.

• Improved enterprise-wide data-driven decision-making by aligning analytics strategy, modern data architecture, and advanced modeling capabilities with executive priorities and long-term business objectives.

• Established a centralized, governed Snowflake data foundation by consolidating fragmented analytical datasets, eliminating metric discrepancies, and improving reporting consistency for 8–10 core analysts.

• Accelerated analyst productivity and reporting reliability by prioritizing the most frequently used organizational datasets and reducing time spent cleaning, restructuring, engineering features, and reconciling inconsistent data sources.

• Strengthened executive-level insight delivery by leading requirements discovery and analytical design for planned Marketing, CRM, and ERP platform implementations.

• Advanced analytics usability for technical and non-technical stakeholders by evaluating and piloting generative AI use cases within Power BI and related analytics tools to improve insight generation, narrative clarity, and dashboard usability.

Chicago, IL US

Senior Manager, Customer Data Analytics

Veritas Technologies

September 2022 — December 2024

Chicago, IL US

Arctera was split off from Veritas Technologies in December 2024 and Veritas Technologies was acquired by Cohesity.

Drove the companywide view of customer engagement, product adoption, and leading indicators of customer defection to shape the customer strategy.

• Reduced annual customer churn from approximately 20% to 15% during rollout by designing and operationalizing an automated XGBoost churn prediction model that tracked ARR health and estimated customer risk over a 12-month horizon.

• Improved visibility into churn drivers by applying automated feature selection, hyperparameter optimization, and SHAP analysis to identify the most impactful risk factors for each customer.

• Revealed a key retention insight by identifying that customers with no technical support cases in the prior year showed elevated churn risk, enabling more informed outreach and engagement strategies.

• Expanded cross-sell targeting for a premium Technical Account Manager service by modeling ideal customer profiles and ranking high-propensity prospects based on similarity to existing customers and using a decision tree model.

• Increased premium service adoption opportunities by generating quarterly target lists focused on renewal timing and identifying customers who were strong candidates for service inclusion.

• Developed a structured premium service pricing model by analyzing contract pricing patterns, customer portfolio characteristics, and total ARR to define a more consistent and equitable pricing framework.

• Advanced analytical standards across teams by establishing and facilitating a cross-functional analytics community of practice for data scientists and analysts, enabling knowledge sharing, peer review, and consistent methodology adoption.

Solutions Consultant

Alteryx

November 2021 — September 2022

Chicago, IL US

Data science specialist in the Center of Competence responsible for working with and enabling Sales Engineers to talk about data science and machine learning using the suite of Alteryx tools and products.

• Equipped up to 100 Alteryx Inspire 2022 attendees with practical linear optimization knowledge by designing, developing, and delivering a 1-hour advanced training course focused on identifying and solving optimization problems in Alteryx Designer.

• Strengthened enterprise sales positioning as a data science subject matter expert, helping account teams align machine learning, automation, and analytics use cases with complex business needs.

Senior Data Science Manager

Braviant Holdings

November 2020 — November 2021

Chicago, IL US

Led a team that developed and deployed predictive models for use in direct marketing campaigns and worked directly with executive stakeholders to craft and implement campaign strategies that balanced response rates and expected customer lifetime values.

• Surfaced 1,000-1,500 high-propensity loan prospects per campaign by developing predictive targeting models that assessed response likelihood and approval potential, guiding direct marketing decisions.

• Improved campaign response rates to approximately 0.5% by optimizing customer selection criteria across response propensity, credit risk, and repayment likelihood to support profitable direct marketing programs.

• Increased accuracy and control over prospect selection by organizing disjointed campaign assets, documenting code, removing inactive logic, and creating standardized templates for each model type and end-to-end campaign process.

• Enabled faster model improvement and monitoring by implementing a standardized data science development methodology covering model design, validation, deployment, retraining, and performance management.

• Supported expanded acquisition modeling capabilities by simplifying the code base and creating a more reliable process for evaluating new credit bureau data sources.

Senior Data & AI Scientist

EY

May 2017 — September 2020

Chicago, IL US

EY Consulting | Technology | Data & Analytics

Delivered advanced analytics consulting engagements by balancing technical execution, stakeholder management, delivery quality, and project economics.

• Improved accounts receivable forecasting accuracy by 90%+ for a major U.S. retailer by leading 3 data scientists in developing daily forecasting models across a 4-week horizon.

• Enabled near real-time workforce insights through PX360 by leading 4 offshore data scientists in developing a machine learning framework that surfaced trending KPIs and key employee experience drivers.

• Developed a highly accurate, comprehensive internal forecasting solution that improved chargeable hours management and resource allocation.

• Advanced distributed data science team capability by managing direct reports through performance reviews, career development planning, promotion processes, and Python-based development coordination across the U.S. and India.

Advanced Analytics Specialist

IBM Corp.

September 2014 — May 2017

Chicago, IL US

IBM Corporate Marketing | Digital Business Group

Designed and scaled enterprise analytics solutions by partnering with global marketing, sales, and technology stakeholders to strengthen data reliability, reporting consistency, governance alignment, and sustained business impact.

• Drove more than $600 million in annual revenue by developing a highly scalable end to end cross-selling recommendation solution that guided sellers worldwide across IBM’s thousands of products.

• Enhanced IBM Cloud retention strategy by developing predictive models that analyzed behavioral, engagement, and usage signals to identify at risk customers, reveal churn drivers, and guide targeted retention actions.

• Improved marketing measurements and sales decisions by building a machine learning solution that separated human website traffic from bots, spiders, and web scrapers.

Senior Managing Consultant

IBM Corp.

November 2009 — August 2014

Chicago, IL US

IBM Global Business Services | Advanced Analytics & Optimization

Served as the analytical lead for various integration and innovation projects and acted as an internal expert for analytical solutions, developed training guidelines, and established predictive analytics best practices.

• Developed and deployed “Keys to the Match”, a statistical model for the four Grand Slam tournaments that quantified player performance patterns and strategic factors for use on the tournament websites and in ESPN’s live broadcasts over multiple years.

• Generated 403% ROI for a major auto insurance client by deploying early fraud detection and subrogation analytics that improved claims accuracy, reduced leakage, and increased operational efficiency.

• Identified substantial cost-savings opportunities for a multinational oil and gas client by applying big data architectures, advanced analytics, and MPP technologies to complex operational and financial datasets.

• Developed predictive models across 9M+ prospects at ADP, identifying that the top 5% were approximately 5x more likely to purchase payroll services and assisted in establishing an internal Analytics Center of Excellence.

• Architected an early-warning analytics system for Gwinnett County Public Schools to identify students at risk of not being college-ready at graduation, enabling targeted remedial action planning.

• Served as a leader in helping to identify and foster new talent and in hiring experienced data scientists.

• Functioned as an internal expert to develop training on the deployment of analytical solutions and best practices in predictive analytics.

Senior Consultant

SPSS, Inc. (now part of IBM Corp.)

March 2009 — October 2009

Chicago, IL US

Professional Services | Predictive Analytics

Relocated from SPSS Denmark to work at the SPSS, Inc. headquarters in Chicago, IL, in early 2009 as an intra-company transfer.

• Served as a principal data science consultant with a deep expertise in deployment of real-time scoring solutions before SPSS, Inc. was acquired by IBM Corp. in October 2009.

Consultant, Sales Engineer, and Training Instructor

SPSS Denmark (now part of IBM Corp.)

March 1999 — February 2009

Primarily served as a statistical and predictive analytics consultant, but also frequently participated in sales calls and delivered end-user training in both statistics, data mining, survey methodology, and how to use the SPSS software.

Copenhagen, Denmark

“My association with Kenneth has spanned more than 10 years and is rooted in the foundation of data mining. Kenneth and I worked together at SPSS answering some of industries most difficult questions. Kenneth's skills were then superior and his ability to decompose business ambiguity was unmatched. We recently had the opportunity to develop and deploy an innovation lab for a global oil and gas corporation. Kenneth led the deep analytic function of the team and was focused on solving some very difficult problems by blending stochastic and physical models. Stochastic modeling is relatively new to petroleum engineering professionals yet Kenneth was able to bridge that gap and deliver significant results to a typically skeptical audience. His work lead specifically to process changes in the manufacturing of alternative fuels, operational changes during casing operations, and timely operational shifts in oilfield operational models.
Kenneth embodies the best qualities of a data scientist whilst being truly adaptive to the dynamic industrial environment. ”

Jeff Pohlman
Associate Partner, Petroleum Strategy and Analytics
IBM Corp.

Testimonial

Project Approach

CRISP-DM is a project methodology tailored for data mining and predictive analytics initiatives and I use this with some modifications for nearly all projects.

Kenneth A Jensen

A data science practitioner who will deliver results at every stage of the analytics lifecycle