My Strive Story: Amanda Jeffers

Strive Consulting wouldn’t be the company it is today without the inspiring group of employees who make up our workforce. We pride ourselves on hiring individuals who bring a diverse group of skills and backgrounds to the table, which set our organization up for success.

Throughout our ‘My Strive Story’ Interview platform, we sit down with members from our different teams within the organization to find out where they came from, why they chose Strive, and what makes their experience here special.

Amanda Jeffers

Senior Program Coordinator

Fairfax, VA

Tell us about yourself.

I’m Amanda, I have been with Strive for almost 5 years now. I started my career in HR and am currently a Senior Program Coordinator for one of Strive’s largest clients. I was born and raised in New York, which I fully blame for my straightforwardness and inability to walk any slower than a jog. I went to a tiny liberal arts college in Pennsylvania, called Lafayette College, where I majored in Geology. Don’t ask me why. I moved to Dallas after college to live with my now husband, Phil. It was a lovely change to be surrounded by slower paced, friendly people. We are now living in Fairfax, VA and spend most of our weekends hiking with our 3-year-old rescue dog, Zoe, who looks a little too similar to a wolf. 

What attracted you to Strive?

When I first moved to Dallas, I worked for a company that forced you to memorize their Core Values and employees would be randomly asked by superiors to recite them at any time, in front of the entire company. Needless to say, this company did not oblige to these values. After interviewing with Strive, I immediately knew they were different. No one needed to memorize the Core Values because every employee lived by them, naturally, every day. It was so new to me to see an office filled with so many talented and competitive people who still celebrate every small win and constantly support each other’s successes. It’s a true family culture and it’s magnetic. Not to mention, I happened to start right before the annual Holiday Party and let me tell you, this exceeded every expectation I’ve ever had for an office party. After attending that, I knew I had found my place.  

What keeps you at Strive?

Working at Strive in both the pre-Pandemic and post-Pandemic world has furthered my gratitude for this company. It’s been admirable to watch a company stay so open and flexible to whatever changes came about while ensuring that the safety and wellbeing of the employees and clients always came first. This went above and beyond for me personally. Early in the pandemic my husband was furloughed and was out of work for a few months. After a lot of stress and hard work he able to land an incredible opportunity, but in Fairfax, Virginia. I was not ready to give up my career with Strive but also knew Phil needed to work, and that this was a life changing opportunity for him. My boss, Jennifer Aiken, and the leaders at Strive were SO accommodating during this transition and allowed me to work remotely while we waited to see how the Pandemic was going to unfold. 2 years later, I am still working from Virginia and flying back to Dallas as often as I can (mostly to spend time with all the people I have come to call my second family over the last few years). I am so appreciative that Strive has allowed me to work remotely, and it encourages me to continually work hard and make an impact, even in a different state. 

What makes Strive stand above the rest?

At Strive, it doesn’t matter what department you work in, what office you work from, or how many years you’ve been there, every employee has the opportunity to have an impact on the company. Throughout the year, there are a number of ways that an employee can express their opinions and discuss what is going well, and what we need to improve on. The best part is, this goes directly to leadership, and I know it is heard. Even during the lockdown, our President, Brian Ganser, and the leadership team made themselves accessible by having video calls with every employee to check in. They do this because they care about their employees and want everyone to have the ability to play a part in making Strive as successful as it can be.  

What are you looking forward to in the future?

In the past few years, we have had a lot of incredible growth and I cannot wait to watch our company have success as we expand into new markets. Our adaptability and determination have kept us moving consistently forward and upward. The Planet Group acquisition has been such an exciting step for our future, and I can’t wait to see how much more impact we can have! 

Interested in joining Strive?

Here at Strive Consulting, we foster an active, innovative culture, providing the coaching, mentoring, and support our employees need to work at the top of their game and succeed personally and professionally. Check out our Careers page for open roles and opportunities within Strive. We’re hiring!

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Categories: Culture, MyStriveStory

Blog Series: Steps to building an Artificial Intelligence & Machine Learning capability

As part of our 3-part series, our next post provides guidance on how to build an Artificial Intelligence & Machine Learning (AI/ML) capability for the first time. In case you missed our post last week, the first article covered three items required before beginning development on an AI/ML initiative. Moving forward, we’ll now focus on the steps to help prepare for development, recommendations on a development and model selection approach, and how to communicate results to stakeholders.

Set a specific objective.

Baseline stakeholder expectations by setting a very clear objective for the project. This will prevent scope creep during development and be more likely to produce results that are meaningful. One example may be to identify typical transactions, or fraudulent, made in 2019 by a group of customers. Can the new capability identify fraud where it was formerly not found? Are there trends you never discovered before? With these new insights, how much loss is avoided by the organization?

When setting the objective, ensure you clarify scope enough that results can be shown to prove the AI/ML capability works. Establish key performance parameters to measure performance, and at the same time, allow a common understanding on what success looks like.

Identify where the Artificial Intelligence & Machine Learning capability will live.

Working with Operations from the project onset prepares internal partners for possible changes to current systems. Their early inclusion enables a smoother handoff and the possibility of a more natural integration into existing systems. The following are a few approaches to consider; keep the AI/ML capability in a separate sandbox until performance is proved out over time, run the capability in parallel with current functionality, or replace/supplement the current functions with the new AI/ML capability. Depending on the degree of integration required, you may want to go as far as enlisting a representative from Operations as a stakeholder.

Prepare for development.

Configuration management for both data and software/models is crucial. If multiple trials are run with various models and sets of data, poorly managing that complexity can lead to costly and time-consuming mistakes. The flavor of development environment is not as important and varies widely across the breadth of data scientists and Artificial Intelligence & Machine Learning practitioners. As your team begins to aggregate data, know that the data preparation may take a significant amount of time depending on the data quality. To give a perspective on the time required for data prep, Anaconda surveyed data scientists and found that 45% of their time is still spent on data loading and data cleansing. 1

Establish the development approach.

When developing a POC capability, the best approach is iterative and adaptable, and will likely take many trials for success. Establish checkpoints for checking on POC performance and aim to produce a minimum viable product from the first iteration on. An agile methodology lends itself well to this type of effort, with the degree of process formality dependent on the appetite of the organization. Look at each iteration as an experiment and use each hypothesis for each iteration as an avenue to incorporate stakeholder expectations.

Select the software package or tool.

The amount of software development required when building a POC varies greatly depending on the package or tool selected. Free packages generally have a larger lift either setting up your environment, or for preparing the data and visualizing it. The most popular programming languages and packages to use are Python [SciPy, Scikit-learn, TensorFlow, etc.], R [randomforest, CARET, KernLab etc.] and JAVA [WEKA, Java-ML, etc.] but require time and expertise to set up and utilize. Tools like Alteryx are easy to use right out of the box and are great for someone not wanting to code but needing more power than Excel. The expertise of resources, budget available and the timeline desired for results will drive which packages and tools work best.

Develop the Artificial Intelligence& Machine Learning capability.  

The specific model to use is driven by the data that is available, objectives and the expertise level of the practitioner. As a best practice, most practitioners start with a simpler model to baseline performance. Complexity is increased if there is insufficient performance on the business objectives metrics. If performance is not as expected after a few iterations, do not give up hope as there may be promise in expanding the dataset to adjacent features or in utilizing another AI/ML method.

Communicate results.

Circle back on KPIs identified during the project onset showing any progress made. Compare the business performance from a current state perspective to the estimated performance provided by the AI/ML capability. Share how the new capability will work in the operational system, if adopted. If removing or reducing manual labor, highlight the extent of time savings for targeted users. If there appears to be lift, provide the estimated ROI once operational.

Next up in our Artificial Intelligence & Machine Learning Blog Series

Part 3 and final portion, where we’ll guide you through the steps of transitioning the AI/ML capability to operations, establishing maintenance routines, and ensuring performance continues to meet requirements for your business.

Connect with Strive!

Here at Strive Consulting, our subject matter experts’ team up with you to understand your core business needs, while taking a deeper dive into your organization’s growth strategy. Whether you’re interested in AI/ML implementation or an overall data and analytics assessment, Strive Consulting is dedicated to being your partner, committed to success.


Blog Series: Taking the First Step with Artificial Intelligence & Machine Learning

Artificial Intelligence and Machine Learning (AI/ML) technology continue to be increasingly accessible with a lower barrier of entry to newcomers. In recent posts, Gartner identifies that one in ten enterprises now use ten or more Artificial Intelligence applications, with the top use cases being chatbots, process optimization, and fraud analysis. 1 Of the companies reporting Machine Learning usage, Algorithmia identified the following area benefits: reducing company costs, generating customer insights and intelligence, and improving customer experiences. 2

Have you been asked to lead an Artificial Intelligence and Machine Learning initiative to solve a business problem?

If so, you’re at the beginning of an exciting journey in leveraging transformational technology. To give you an idea of potential return, let’s look at Highmark Inc.’s Financial Investigations and Provider Review Department. Highmark successfully applied AI to save over $260 million related to fraud, waste, and abuse in the year 2019 and continues to use AI to enhance fraud identification and prevention to catch potentially fraudulent activities today. 3

Because many have walked the path before you, successful approaches to incorporating AI and ML capabilities are readily available. This blog series will guide you through that process, while highlighting best practices and potential pitfalls. Our first article starts by walking through three key components that must be present for a successful AI/ML initiative.

The following items are considered as entrance criteria:

A specific business need is identified.

  • For the first AI/ML project, it is best to choose a well-defined bite-sized proof-of-concept (POC) along with a key metric to evaluate the level of success. A succinct problem statement and hypothesis on outcome(s) will align the team and set stakeholder expectations with each iteration of the POC. Success on a POC enables further endeavors of a more aggressive nature.
  • If a specific objective is not identified, the project has the potential to fail based on the growing or changing scope, or even worse, misaligned expectations.

Data exists to support the business objective.

  • A full data warehouse does not need to be present, but enough data of sufficient quality must be available to build the knowledge base needed for training the AI/ML capability. Be sure to consider any support data that may be needed to fully address the objective. If data gaps are identified, there may still be a chance to produce a meaningful result, but with a reduced scope of application. To reduce risk, the best first attempt for use cases have the required full dataset available within a single source of truth, or the data is available via existing pipelines.
  • Data volume is a primary requirement, closely followed by data quality. In a study that Researchscape International and Trifacta performed, one of the greatest challenges the respondents faced for AI implementation was data accuracy, with only 26% of respondents reporting that their data is completely accurate. Additionally, poor data quality negatively impacts AI/ML initiatives, with more than one-third reporting that projects took longer (38%), were more expensive (36%), or did not achieve anticipated results (33%). 4
  • If the current state of your data is not known, you may want to perform a data assessment before pursuing the AI/ML initiative further.

A culture of making data-driven decision making is present.

  • An AI/ML capability may have the potential to disrupt your business, but without an appetite to steer decisions based on the algorithm outcomes, the initiative will fall flat. If there is skepticism, show the estimated ROI and a plan to kick the tires with A/B testing. An adoption roadmap with milestones and corresponding results will ensure confidence that true diligence is being taken and result in reduced uncertainty.

If these criteria are met, the odds of a successful AI/ML implementation are tenfold.

Tune in next week!

Part two of this series will guide you through the steps of developing the AI/ML capability, from setting an objecting all the way through to managing and understanding your results. Make sure to check out Strive’s Insights page for additional postings and information.

Connect with Strive.

Here at Strive Consulting, our subject matter experts’ team up with you to understand your core business needs, while taking a deeper dive into your organization’s growth plan. Whether you’re interested in AI/ML implementation or a overall data assessment, Strive Consulting is dedicated to being your partner and committed to success.


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