Teneika Askew

US Navy

The consultant has become the client! I've returned as Product Manager and leader in the Navy's first data focused consulting division, Data and Digital Transformation. I will be bringing my entrepreneurial spirt to build, launch and direct the Data Science as a Service and Analytics Enablement portfolio of products and services.

Stack: Databricks, AWS

Product Director - Data Science & Analytics Enablement 

CompTIA

• As a CAC appointee, I provide advice and guidance to CompTIA regarding the strategic direction of the industry's leading certifications related to data science & machine learning.

• As a SME, I contribute to the development phases of CompTIA’s Data Science+ certification exam. This includes participating in Job Task Analysis (JTA) workshops, helping to identify exam domains & objectives, and assisting with item writing & review.

Data Certification Advisory Committee (CAC)

Clicked

Artificial Intelligence Coach and Marketing Consultant

Correlation One

Empowerment is a 4-5 month, high-intensity, part-time program teaching data skills through real world business cases and live, business provided or fellow researched datasets while solving AI challenges.

Introduced 7+ sponsors/partners . (OneTen, McKinsey, Hulu, Accenture, JFF, etc..) after extensive market research and talent development opportunities such as college credit.

Data Science Instructor, Head Teaching Assistant and Strategic Program Advisor

Receive Top Teaching Assistant recognition across 50 Teaching Assistants!
Provides mentorship and support for 50+ fellows across 7 projects. Rated in top 5% among Teaching Assistant by fellow reviews.
Curates custom curriculum for the Data Science for All Product, tailored specifically for Product Analytics, Tableau and professional development workshops and lectures. Provides instruction to 750+ fellows for Tableau, Algorithmic Bias and Product tracks on Saturday's.

Data Science Instructor, Teaching Assistant and Strategic Program Advisor

Partners with product, curriculum and program leaders to achieve learning objectives of 4+ programs and optimal functionality of tools/technology in the proprietary LMS, such as Grading/Attendance, Insights, Jupyter Notebooks, Workspaces, Forums, and lesson outlines/content.

Serves as a change agent, championing new and innovational customer- and industry-focused data informed products, driving inclusive and equitable data science, AI/ML education strategies across 6+ cohorts,  aligning to industry and partner talent objective and metrics.

Strategic Program Advisor

Samsung Innovation Campus

Artificial Intelligence Coach

Uplimit

Designed and instructed Visualization with Tableau 4-week course.

Analytics Coach (Tableau)

Codecademy

Senior Curriculum Developer (Product and Project Management)

SkillUp

Provided data science expertise and career prep guidance and videos for several IBM Coursera courses.

Data Science SME (IBM Coursera Courses)

Bill & Melinda Gates Foundation

Partnered with ABG to provide strategy and key recommendations for an organization wide analytics enablement, cloud deployment initiative. Delivered tailored workshops to help the organization reach a common data model supporting mission driven insights, KPIs and roadmaps to Bill and Melinda Gates funded non-profits in the South Africa region.
Tools: Microsoft 365, Azure, Power BI, Oracle

Analytics Consultant

Mozilla

Teneika developed the global People Analytics function from ideation, providing delivery of self-service analytics tools, insight-driven solutions and advisory services for Mozilla’s workforce. She specializes in applying storytelling and analytics to aid the SVP, Steering Committee, HRBPs and leadership in data driven decision-making of talent-related questions. Tools: Workday, Greenhouse, Power BI, Tableau, BigQuery, SQL, M Language, DAX, AWS Redshift, Tableau Server, CultureAmp
• Quickly developed relationship as trusted advisor to Executive Leadership team and served as Product Owner for an implementation; identified key risks in ETL processes, resulting in savings within 60 days of hire
• Designed and developed a comprehensive analytics modernization roadmap and interactive dashboards for critical executive-level meetings, allowing visibility into trends, headcount, attrition, culture surveys, vacancy, hires, and market insights
• Partnered with SVPs of Talent Acquisition, People, Diversity & Inclusion and business leaders to drive data-driven decisions and influence program and policy changes for Racial Justice Commitments, within 90 days of hire; 5% increase in Black/Latinx talent
• Managed multiple global projects and stakeholders involving delivery self-service analytics solutions in Tableau / Tableau Server with disparate data sources such as a Talent Acquisition Dashboard and D&I Scorecard
• Created a workforce reduction and planning model leveraging PowerQuery as a strategic tool for reduction in force decisions and talent related concerns that lead to business impact – resulted in 31% reduction in planning hours
• Led future of work effort to reclassify job families into new “Tech/Non-Tech” definition, resulting in 14% change across company
• Fully launched the global People Analytics function, frameworks for identifying business problems, the governance model for data, product and people roadmaps and 5 work products, within 6 months of hire

Head of People Analytics

Analytics Product Manager | AI/ML | Social Entrepreneur | Data Scientist | Mentor | Big 4 | DS4A | Spartan Racer

Teneika Askew is an Analytics Leader with more than 10 years of experience providing analytics, business process improvement and consulting services. She specializes in data analyses around financial, talent and operational data and driving workforce strategies for Fortune 500 companies.

Teneika uses Power BI, Tableau, SQL and Python to create 100% data-driven, innovative insights using organizational data. She also trains colleagues and the communities she serves with open data made available through the DATA Act.

Teneika also has extensive experience with business analysis for commercial, not for profit, and governmental entities. Her strengths are in performing complex analyses to identify and understand processes, gaps and transactions to answer difficult to solve problems.

Awards: Globant US Digital Leader. Women of Color in STEM - Technology All Star Award. Values in Practice. Applaud Awards (3X). Performance and Team Awards (6X). She Rocks the World, Fearless Leader. Diversity & Inclusion Driver Award. Grace Hopper Women of Color in Tech Scholar. People Analytics World Speaker. Society of Women Engineers Speaker. Distinguished Scholar and Honors level in Data Science for All fellowship(s). Top Teaching Assistant.

Certifications: Microsoft Data Analyst, Certified Scrum Master, Tableau Desktop Specialist, AWS Solution Architect. COBIT 5, ITIL, Agile Certified Practitioner and Microsoft Certified Trainer

Fellowships: Data Science for All - Empowerment and Data Science for All - Women's Summit

Recognized as US Digital Leader by Globant in the annual edition of the Globant Awards - Women that Build,  a global recognition that seeks to change the reality of the industry and reduce the gender gap, highlighting professionals who inspire, who build, who lead and help create change. 
http://womenawards.globant.com/3335/Teneika-Askew
This award recognizes innovative women who are leaders in their organizations and who have been impacting change in the IT industry for at least 10 years.

Featured in Business Insider, Tech Crunch and eFinancial.

teneikaaskew.com | @teneikaask_you on Twitter

Group Product Director - Data Science & Analytics Enablement

🎯 Mark Freeman II

Gable

• Joined as employee-one to lead GTM at Gable to help us reach our first customers.

• Reported directly to the CEO and led the development and execution the go-to-market
strategy which resulted in a successful launch, 4000+ leads, and 300+ meetings with
senior data engineers and leaders, ranging from scaleups to Fortune 500 companies—
this was integral for our ability to raise a Series A. (https://bit.ly/4eDwbPE)

• Co-authored the O’Reilly book "Data Contracts: Developing Production Grade Pipelines at Scale,"
contributing all the technical chapters, and positioning Gable as thought leaders of an
emerging category within the data market. (https://bit.ly/3XIu4mF)

• Served as a technical expert by presenting at conferences on data engineering topics and providing thought leadership via blogs.

• Built Gable’s marketing and sales stack with an emphasis on data capture, automation,
and analytics that enabled automated qualification, conversion into meetings, and nurture
campaigns for leads—while establishing the underlying data model.

• Served as a sales engineer, mapping technical requirements to Gable’s product roadmap,
building champions among directors plus data leaders, creating data strategy decks for
clients, and closing multiple accounts.

• Served as a data scientist, providing insights and visualizations for Gable’s executive team
for presenting sales metrics to the board and potential investors.

Tech Lead, GTM Engineering

On the Mark Data

I help brands connect to data professionals via content strategy, GTM strategy, and influencer marketing. Learn more at onthemarkdata.com.

• Built a brand with an audience of 40k+ data professionals via social media content, technical blogs, and video-- which has harnessed over 14MM impressions.

• Established the weekly Scaling DataOps newsletter, with 7k+ subscribers, where I interview data leaders and executives on data best practices in scaling data infrastructure.

• Built a revenue stream around sponsored content from top data brands such as Nvidia, SuperDataScience, Scale AI, and much more.

Founder

O'Reilly

Currently writing a book on data contracts.

⭐️ "This book has the potential to become the definitive guide for data contracts! I can't wait to dive into the rest of it and unlock even more valuable insights!"

⭐️ "The initial read is clear and provide a good potted history of the data problem and how it has organically morphed due to so many factors. I can't wait for the rest of the book"

⭐️ "This book has the potential to become the bible of data contracts especially as the world moves towards intelligent applications. Data is essential to applications that use AI, and having a good contract is paramount towards establishing trust between systems and having a seamless experience. Looking forward to the full publication!"

Author - Data Contracts: Developing Production Grade Pipelines at Scale

LinkedIn

⭐️ "Took a few hours out of my Saturday to dive into data engineering with DBT, and had the opportunity to pair program with Mark... Great work on the new course, Mark. I sure learned a lot, and I'm confident others will benefit from it as well. As someone who's relatively new to data engineering, this short course was an eye-opener. It expanded my understanding of the scalability and possibilities within the field."

⭐️ "I completed Mark's course over the weekend and thought it was a GREAT introduction to using DBT. For people looking for a low-stakes, easy-to-follow introduction to DBT as a tool for data engineering, I think you would be pressed to find something more effective."

⭐️ "I would like to thank Mark... for creating such a comprehensive, engaging & pure hands on course on dbt. He explained the concepts clearly & demonstrated them with practical examples and exercises."

⭐️ "I was excited when I saw this course on your page, i know this is going to be the course I needed to start my DBT journey and you did not disappoint, Thank you"

LinkedIn Learning Instructor, Data Engineering

Humu

• Build ETL pipelines to bring third-party data sourced from APIs to combine with our product data, creating rich data assets to drive product feature creation and customer analytics.

• Built buy-in, developed the transition plan, and led the migration of the orchestration of data transformations from manually created scheduled queries to data build tool (dbt)-- which enabled version control of our semantic layer, data quality checks, and data discoverability of data assets within our data warehouse.

• Led the development of an internal data quality engine to increase test coverage of core data pipelines and implement data observability.

Senior Data Engineer

Driving forward the organization’s data strategy by:
• 1) Conducting user interviews to understand the organization’s data pain points.
• 2) Identifying the need for data marts to increase data access.
• 3) Scoping overhaul of our data pipelines and warehouse.
• 4) Sourcing data ingestion vendors.
• 5) Fostering leadership buy-in via building minimum viable products.

Senior Data Scientist (Data Engineering)

• Led the development of the Data Science team’s custom R package and managed the internal distribution and upgrading.

• Implemented CI/CD utilizing Docker and CircleCI to ensure changes to the Data Science codebase passes tests and prevent breaks to our internal packages.

• Assisted in doubling the Data Science team via interviewing, onboarding, and technical mentorship for upskilling colleagues.

Senior Data Scientist

• Developed and put into production an NLP pipeline via Python that takes raw text data, classifies respective texts' themes, and ranks and assigns text to relevant insights within customer reports (https://bit.ly/3yAeBqu).

• Designed and put into production via Python an algorithm to classify user activity—output became org’s main product health metric and was validated with both statistical significance and UX research interviews (https://bit.ly/3yt5QhY).

• Support the VP of Product in strategic product decisions by refining business questions, conducting log analytics via SQL and R, and creating reports shared org-wide.

Data Scientist

IndexoRx

• Made it to the final round of interviews for Y Combinator, where we presented our pitch deck to their investment team and described the business opportunity within the pharmacy space in reducing the burden of DIR fees.

• Developed an MVP of our data product that utilized analytics and machine learning to rank users at risk for lowered medication adherence and forecast DIR fees.

• Conducted deep market research on pharmacy business models, pharmacy supply chains, and PBM reimbursement processes.

Founder, Chief Data Officer

Bay Area Community Health Advisory Council

• Lead organization’s evaluation efforts through developing a standardized survey utilizing CSAT and NPS scores, transitioning administration from paper to Google Forms, and providing analysis and data visualizations of services data—resulting in a 24% increase in survey response rate and 2 reports used by org’s Executive Director for fundraising

Member / Evaluation & Analytics Committee Chair

Verana Health

• Utilize deep understanding of medical claims and EHR data, in conjunction with strong SQL and PySpark skills, to work with data that contains over 63 million patients, 274 million patient encounters, and over 10 billion data points amongst 55 separate EHRs

• Developed and deployed into production an algorithm via Apache Spark that classified insurance types for over 250 million records, utilizing unstructured EHR data, that improved both accuracy from 40% to 84% and reduced null values from 16% to <1%

• Support sales team in 21 potential contracts by determining viability of our data assets through defining research and business questions, developing SQL and PySpark code to build cohorts, providing insights on quality issues within data, and creating data reports to communicate findings to non-technical staff

• Conduct an algorithm assessment on a key variable and identified errors impacting 1.3 billion records, as well as provide recommendations to the algorithms team to rectify the error—assessment was utilized to help certify new data architecture

Data Scientist (Data Quality)

Stanford University School of Medicine

• Developed R script for department’s ETL process, that transforms data into cleaned variables and removed duplicate observations based on defined logic—reducing data cleaning time of analysts by 10 hours per project

• Completed an exploratory analysis through conducting 48 multiple regression models to understand relationship between biometric data and self-reported wellbeing data, thus identifying 3 viable hypotheses to further investigate and ensuring the lab diverts resources to impactful studies 

• Updated methods of a previous propensity score match analysis, lowering the SDM for all variables from 0.17-0.21 to <0.10, resulting in the analysis now meeting required match assumptions and improving the manuscript strength for publishing: matching accounts for observed confounder bias

Data Analyst

Collective Health

• Developed and operationalized a Python script, utilizing pandas, that identifies discrepancies between insurance enrollment data and network partner data— reduced manual work process time from 98 to 16.3 hours a year (time saved grows with number of added clients)

• Developed and operationalized a Python script, utilizing pandas, that takes 3 sources of data and creates custom reports for respective insurance partners’ specification— reduced manual work process time from 52 to 4.3 hours a year

• Utilize SQL to pull necessary data to resolve insurance eligibility issues and other related operations work

Operations Associate

onthemarkdata.com | Senior data engineer helping brands with GTM and content strategy.

I'm a community health advocate turned data engineer interested in the intersection of social impact, business, and technology. My life’s mission is to improve the well-being of as many people as possible through data—especially among those marginalized.
 
As the founder of On the Mark Data, I use this platform to help brands connect to data professionals through captivating content.
 
I received my M.S. from the Stanford School of Medicine and I'm also certified in Entrepreneurship and Innovation from the Stanford Graduate School of Business. In addition, I have worked within numerous startups where I have put machine learning models into production, integrated data analytics into products, and led migrations to improve data infrastructure.
 
The culmination of my technical skills, content success, and experience within startups would make me an asset whether you are looking to create a successful content strategy, engage in influencer marketing campaigns, or develop a go-to-market strategy for your startup.

You can learn more about opportunities to collaborate, my media appearances, or my content at onthemarkdata.com.

• Joined as employee one to lead GTM at Gable to help us reach our first customers.
• Worked with the CEO to establish our GTM strategy, content strategy, and messaging for technical audiences.
• Building internal data products to drive our GTM strategy and sales.

It didn't feel real until I had the physical copy of the book in my hands, but I'm officially a published author!

This is just such a full circle moment for me as I was reading these O'Reilly animal books when I was dreaming of making a career in data.

If you told me nearly 7 years ago that I would have my name on one I wouldn't believe you!

This represents about two years of life of nights and weekends on top of helping build a startup (Gable) and my own company (On the Mark Data).

Writing a book and building a company was hard... but I would easily do it all over again.

Hundreds of user interviews and discovery calls informed us what would be meaningful in the book.

While the book provided us clear synthesis of what we were seeing in industry.

This created a symbiotic relationship where we would build, write, get feedback, and repeat.

A huge shoutout to the O'Reilly team and my co-authors Chad and Ben!

An even bigger shoutout to everyone who supported us, as it wouldn't have been possible without you all.

Data Engineer | Tech Lead @ Gable.ai | O’Reilly Author: Data Contracts | LinkedIn [in]structor (30k+ Learners) | Founder @ On the Mark Data

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Here's my tech stack for the dev environments I create for LinkedIn Learning courses (all open source) that you can use for yourself:

🐋 Dev Environment:
- Docker Compose - Containers and configs
- VSCode Dev Containers - IDE in container
- GitHub CodeSpaces - Browser cloud compute

📦 Databases:
- Postgres - Transactional Database
- Minio - Data Lake
- DuckDB - Analytical Database

⚙️ Ingestion + Orchestration + Logs:
- Python Scripts - Simplicity over a tool
- Data Build Tool - SQL queries on DuckDB
- Alembic - Python-based database migrations
- Psycopg - Interact with postgres via Python

📋 CI/CD:
GitHub Actions - Simple for students
 
📊 Data:
Data[.]gov - Public real-world datasets

👩🏽‍💻 Coding Surface:
Jupyter Notebooks - Quick and iterative
VS Code - Update and implement scripts

This setup is extremely powerful as you have a full data platform that sets up in minutes, it's filled with real-world data, you can query it right away, and you can see the logs. Plus, since we are using GitHub codespaces, it's essentially free to run in the browser with just a couple clicks!

"What do you mean my ROI would only be 1.5x? I need it to be a 4x for it to make sense..."

That brutal honesty was one of the best lessons I've learned in getting adoption and budget for data infrastructure projects.

I spent days making a proposal for a c-suite exec I was trying to sell to. I identified their core problems, was getting multiple meetings, and we were talking about numbers... and that's where I messed up.

My mistake was assuming this was their only problem they were considering for purchasing, and not part of a portfolio of problems they were strategically trying to solve.

I wasn't "competing" against a problem I knew I could clearly solve and generate ROI on. Instead, I was competing with the opportunity cost of not pursuing otherr solutions with various multiples of ROI for different problems.

That meeting completely changed how I think about value to execs. It's funny how obvious it sounds looking back, but I'm hard headed and needed to learn by experience.

Fast forward to today, and I feel like I have had enough reps to dive into the business technicals of what value actually means and map it to my experiences.

This week I'm reading the book Valuation, which was essentially the playbook used by McKinsey consultants. Specifically, I want to take my understanding of what is valuable to a business to next level with economic theory and technical business fundamentals.

I think more technical people should study this as well! Move away from parroting the abstract phrase "we need to focus on driving business value" and instead provide a clear plan to what that actually means and why a leader shoukd choose to invest in your initiative over others.

Allowing others to explore AI >>> Gatekeeping AI

I still remember the first time I saw a neural network work in real-time (shoutout the MarI/O Youtube video by SethBling) and just the magic I felt.

That was the moment I realized I needed to make a career in data and the moment I got over my fear of coding.

So many non-technical, but highly skilled in their domain, professionals are now experiencing exactly what I felt years ago.

Why try and police that?

Yeah, that vibe coded app is absolute crap and one agentic action away from deleting its own databases... but that person shipped something.

That's already more progress than the technical naysayers shouting from the sideline.

taplio

The Battle

Teneika Askew

🎯 Mark Freeman II

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taplio

The Battle

Teneika Askew

🎯 Mark Freeman II

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}🎯 Mark Freeman II's LinkedIn Performance

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Performance Score🎯 Mark Freeman II's LinkedIn Performance