Founder, Chief Data Scientist | May 2025 - Present

After a winding career path, I founded Studio 240, LLC, a data science and AI product consultancy. My passion is for purpose-driven technology that delivers genuine value, not just hype. I believe in empowering tech professionals and leaders to see technology as a service, bringing clarity to chaos for early-stage organizations. I'm excited to align AI vision with impactful execution and help move solutions to higher ground.

Senior Technical Director | August 2003 - March 2005

After a fulfilling experience in strategic product management at EarthOptics, I wanted to return to a role where I more directly engaged with software, engineering, and data science teams. Joining Pixxel as a Senior Technical Director presented an opportunity to work in the Space industry on innovative hyperspectral imaging satellites and gain experience in business development with the US government. It was a great way to leverage my project, product, and people management skills to help cross-functional technical teams deliver high-quality products and services.

At Pixxel, the US Public Sector team focused on driving revenue by selling hyperspectral image products and securing contracts with US Defense, Intelligence, and Civilian agencies. My role involved overseeing the success of data science, ML/AI, and software engineering services and ensuring the delivery of high-quality data. Key responsibilities included stewarding and implementing Pixxel’s technology roadmap to align with US Public Sector market needs and emerging technologies, leading technical team efforts in business development and proposal preparation to secure new opportunities, and managing customer engagement while overseeing the planning and execution of projects to ensure alignment with client requirements and successful program delivery.

Some of my key activities and accomplishments at Pixxel included:

Secured Pixxel’s selection as one of eight companies for a $476M multi-vendor award from NASA’s Commercial SmallSat Data Acquisition program by directing the technical proposal writing effort, implementing effective team coordination processes, and ensuring on-time submission and compliance.

Drove strategic business growth by managing a team of eight to develop compelling analytics use cases for our booth exhibit at the USGIF GEOINT Symposium 2024, resulting in impactful engagement and attracting new customers and business partners.

Actively engaged with eight USG agencies, USG research labs, and strategic partners to develop CRADAs and teaming agreements for R&D in hyperspectral imagery assessment and ML/AI applications, resulting in three signed agreements within one year and enhancing collaboration opportunities.

Delivered a comprehensive report on Pixxel’s satellites, sensors, and access and collection capabilities to the NRO on time by directing an international team of 12 professionals in space mission planning, operations, and remote sensing science, and ensuring contract requirements were met.

Oversaw the successful implementation of hyperspectral imaging data storage in the Spectral NITF Implementation Profile (SNIP) format, ensuring compliance with government requirements for the NGA and NRO.

Strategic Product Manager | April 2023 - August 2023

Following EarthOptics’ Series B funding raise, the company underwent a significant restructuring that moved product management into a separate department under a new Director, leading to rapid team expansion. In recognition of my long tenure with the company and deep familiarity with all product lines, I transitioned into the role of Strategic Product Manager. While quite different from my previous roles, this position allowed me to focus more intently on product strategy and business growth, allowing me to shape the team’s processes for enhanced efficiency.

In this role, I stewarded the strategic roadmap across all product lines to ensure alignment with business goals while promoting cross-departmental visibility and collaboration. I focused on driving explorations of opportunities to provide a clear understanding of market appetite. As the owner of end-to-end activities, I worked to streamline, standardize, and scale our company’s product exploration and development cycle. My top achievements included:

Defined and successfully rolled out our new partner program, establishing a high-margin revenue model that resulted in signing up 38 partners during the first two years.

Analyzed customer order trends and requests alongside market data to define a 1-year vision and scope for the C-Mapper product, enabling product managers and engineers to prioritize development efforts effectively.

Developed a streamlined intake process for managing engineering and product requests, standardizing prioritization across product lines, reducing meeting time by 50%, and enhancing visibility into product decisions while minimizing distractions for engineering teams.

VP, Technical Product Management | April 2022 - April 2023

A few months after EarthOptics’ Series A funding round, a new SVP was brought in to oversee the newly integrated software and data engineering team. Recognizing the need for a leader to head technical product management and unify development efforts among software engineers and data scientists, I eagerly seized the opportunity to officially take on this role and introduce product management frameworks into the organization.

As VP of Technical Product Management, I embedded myself within the engineering team to establish product strategy and priorities. My responsibilities included translating those strategies into technical requirements, coordinating release priorities, and effectively managing agile software teams to drive successful product development and ensure timely execution. In this role, I:

Managed the full project lifecycle to design, develop, and launch EarthOptics’ SoilCollector app and data collection processes, achieving a 20% reduction in database errors and streamlining operations by effectively collaborating with field technicians, program managers, and software engineers.

Launched the Nutrient-Mapper product, providing actionable insights on soil nutrient distribution to growers and improving farm management decisions by managing the entire product development lifecycle, including stakeholder interviews, product requirements definition, and user interface design.

VP, Data Science | May 2021 - April 2022

Sometimes, the paths we take in our careers unfold in unexpected ways, leading to opportunities that shape our professional journeys. After being furloughed at Astraea during the pandemic, I embraced the chance to join EarthOptics as the seventh employee of the seed-funded AgTech startup. In this role, I found the opportunity to provide ML/AI leadership while contributing to an innovative venture focused on bringing real value to growers. The position excited me because it enabled me to apply my expertise in geospatial analytics while utilizing innovative ground-based imaging techniques, aligning with my passion for developing analytical solutions based on sensor data.

As VP of Data Science, I hired, trained, and managed a team of four data scientists and ML engineers to develop scalable data science solutions while also actively contributing as an individual. I quickly established clarity and structure within the team and refined our approach to rapid prototyping. This led to multiple successful demonstrations of our value proposition, which ultimately secured our Series A funding just a few months after I joined. My key accomplishments are:

Developed the ML solution for EarthOptics’ C-Mapper product, utilizing ground-based sensing technology and core samples to deliver high-accuracy, cost-effective carbon stock quantification, enabling landowners to participate in carbon markets.

Hired, trained, and led a team of four data scientists and ML engineers to develop advanced ML models that deliver actionable insights for farm management decisions, driving business growth while fostering a psychologically safe environment that enhances analytical skills and encourages innovation.

Delivered a successful onsite demo of a real-time prescription tillage capability, resulting in a satisfied customer and contract renewal, by designing and integrating an ML solution using ground-based sensor data and the tractor’s onboard command software in just six weeks.

Implemented automated ML deployment pipelines for the C-Mapper and Nutrient-Mapper solutions, enhancing operational efficiency and reducing customer delivery timelines by 80%.

Board Chair | November 2020 - August 2021

Empowered Earth Alliance (EEA) envisions a world where local leaders are empowered to create and implement sustainable solutions for a thriving planet. Its mission centers on equipping these leaders with practical scientific, technical, and business skills to tackle sustainability and climate change challenges effectively. By fostering hands-on collaboration and ownership, EEA aims to bridge the gap between expertise and local decision-making, ensuring that solutions are not only relevant but also adaptable to the unique needs of each community.

I served as the first board chair of EEA and advised on a collaboration with housing officials in Botswana to develop innovative methods for regular data collection and interpretation, facilitating evidence-based planning and policymaking.

Co-founder, VP, Data Science | November 2016 - May 2021

Joining Astraea offered me an incredible opportunity to help establish a new company alongside former colleagues during a transformative period in my career. Initially hesitant to leave a stable corporate job—especially while pregnant with my second child—I embraced the challenge fueled by my passion for the company’s ambitious goals. My background in working with ML/AI techniques and imaging data, along with expertise in radiative transfer, detector technology, and remote sensing methods, set me up to be an effective leader for the initiatives we needed to pursue.

In those early months, my co-founders and I focused on defining our corporate culture and values, which guided us in hiring individuals who would thrive in that environment. As I began to build and lead the data science team, I was excited to leverage my expertise, but I also faced the challenges of becoming a first-time people leader and department head. This experience taught me the importance of fostering a collaborative and inclusive workplace where creativity and innovation can flourish. I learned to prioritize meaningful relationships, direct communication, and empathy, which enhanced team dynamics and empowered my colleagues to contribute their best work. These values became foundational to my leadership philosophy, enabling me to create a supportive environment where individuals could grow and excel.

In my role, I established the strategic roadmap for data science, managing agile projects and consulting services. I defined initiatives for machine learning research, implemented agile development processes, and ensured my team met self-imposed timelines to deliver impactful solutions. I actively hired, trained, and mentored three data scientists to foster their professional growth, enhance team performance, and drive our initiatives forward. Additionally, the small-size nature of the startup allowed me to wear multiple hats, exploring areas such as business development, marketing, operations, and product management, which increased my understanding of the various functions different departments play within an organization and how they work together.

My key accomplishments include:

Architected and developed the large-scale deployment of a multi-step computer vision model using Earth Observation data to locate undocumented cement plants with 84% recall, exceeding customer expectations and delivering on time.

Awarded a $420k grant to develop an asset-level database of heavy industries by facilitating technical exchange meetings and defining the scope of work, achieving a critical milestone of Astraea’s first revenue from a customer.

Delivered a proof-of-concept prototype of the EarthAI platform in eight weeks by defining ML/AI use cases and orchestrating an agile development process across three teams, ultimately securing Astraea’s Series A financing through impactful live demos with investors.

Built a new team of three data scientists, training them in geospatial analysis and satellite imagery while establishing best practices in teamwork through agile development processes, collaborative coding, and self-accountability.

Led the development of a deep learning model and web application in eight weeks to detect utility-scale solar farms with 85% recall using Earth Observation data and computer vision, significantly boosting interest in Astraea’s capabilities among potential investors and customers.

Managed a client project to streamline opportunity prioritization for a solar panel provider by using aerial-based imagery and machine learning to classify roof materials, resulting in a 30% reduction in assessment time and improved targeting of installation sites.

Designed and taught a training program that upskilled 10 conservation scientists at The Nature Conservancy in data science and ML/AI methodologies, enhancing the skill sets of several professionals.

Sponsored a direct report’s Master’s in Analytics capstone project, providing guidance on the scope and ML solution that achieved 90% accuracy in crop classification, leading to her successful graduation, valuable insights for our R&D initiatives, and a company blog article.

Below is a list of selected presentations and publications that came out of my work at Astraea:

Tkachenko, Natalyia, et al. “Global database of cement production assets and upstream suppliers.” Scientific Data, October 13, 2023.

Layman, Courtney, et al. “Predicting Food Insecurity in Zambia Using Satellite Imagery.” Medium, January 24, 2020.

Scott, Kim. “Tackling challenges in AI generalization and scalability.” Trillion-Pixel GeoAI Challenge Workshop, Panel (2019).

Scott, Kim. “Building a cross-industry, geospatial analysis platform for commercial and non-profit customers.” IEEE International Geosciences and Remote Sensing Symposium, Panel (2019).

Layman, Courtney, et al. “Using Satellites to Track Solar Farm Growth.” Medium, August 12, 2019.

Scott, Kim. “Seeking opportunities for innovation and professional growth? Try a tech startup!” IEEE Women in Engineering Forum USA East (2018).

Data Scientist | November 2015 - November 2016

I was excited to join the University of Virginia Health System on a newly formed data science team within the broader healthcare analytics division. We were tasked with discovering innovative ways to improve hospital operations and patient care through data and analytics. During this pivotal transition to electronic medical systems, our team played a crucial role in integrating advanced ML/AI methods into analyzing hospital and patient data. As an individual contributor, my primary focus was leveraging data from physiological bedside monitors and collaborating with stakeholders across the health system to identify and implement other high-impact projects. My top projects and successes included:

Managed a pilot study to build the computing infrastructure for storing, databasing, and analyzing high-volume physiological data from bedside monitors.

Worked with engineers, IT, and doctors to understand the data pipeline, security requirements, and data access needs of end-users.

Created a predictive model and application to enable the administration to evaluate the financial impact on profit margin from proposed changes to operations.

Used descriptive statistics to advise the Chief of Quality and Performance Improvement on five-year trends relating to how in-house medical practitioners prescribed opioids and benzodiazepines.

Data Scientist | September 2014 - November 2015

Joining Elder Research marked a pivotal moment in my career, as it was my first role as a data scientist after transitioning from the field of astronomy and academia. Following six years in graduate school and two postdoctoral positions, I began reflecting on my life and career—especially with my first child on the way. I sought fulfillment in both my personal and professional life. Through numerous informational interviews, I discovered data science, which appealed to me as it merged my experience in data processing and analysis with the opportunity to make a tangible impact across various industries. I was fortunate to join Elder Research, a company that emphasized hiring top talent, fostering respect among colleagues, and delivering high-quality results while prioritizing well-being.

At Elder Research, I was part of the commercial data science team, which provided data science services and product development for clients across diverse industries. This role allowed me to build custom data science solutions tailored to clients’ needs. In addition to technical contributions, I served as the project manager for our team, coordinating workflows, ensuring on-time delivery, and managing client meetings to facilitate smooth communication and collaboration throughout projects. My key achievements were:

Managed the development of a data visualization application to help the R&D team at SolidWorks gain new insights into customer behavior.

Used k-means clustering on software logs to identify user segments for SolidWorks and built a predictive model with 92% accuracy to categorize new users and inform marketing and product decisions.

Presented results at a premier machine learning conference (R. Chin and K. S. Scott. “Driving product improvements and marketing efforts through software usage logs.” Predictive Analytics World for Business, 2015).

Developed an R package to streamline the randomization testing process for statistically evaluating a predictive model’s significance.

Designed a web application for the University of Virginia School of Law admission office that provided an interactive tool for optimizing offers to students most likely to matriculate.

Created training materials and lectured on machine learning techniques for data science practitioner courses custom-made for commercial clients.

Supported the summer internship program by training students on data science concepts and machine learning algorithms.

Postdoctoral Research Fellow | July 2011 - September 2014

I had the privilege of joining the National Radio Astronomy Observatory as a postdoc during the commissioning phase of the Atacama Large Millimeter/submillimeter Array (ALMA), a groundbreaking observatory located in the Atacama desert of northern Chile. Recognized as the most advanced millimeter-wave telescope in the world, ALMA significantly enhances our ability to study celestial phenomena, providing unprecedented insights into the formation of stars, galaxies, and planetary systems. I was excited to take on this role as it perfectly aligned with my expertise in millimeter-wavelength astronomy and my passion for leveraging mathematical techniques in image processing.

As part of the North American ALMA Science Center (NAASC), my postdoc role was dual-focused, allowing me to balance independent research with support for ALMA’s commissioning and operations. My accomplishments include:

Designed a successful proof-of-concept experiment to detect carbon monoxide in infrared-luminous galaxies.

Modeled the far-infrared spectra of more than 2000 galaxies using non-linear least squares optimization to estimate their star formation rates and dust masses.

Published a first-author paper in a peer-reviewed journal (K. S. Scott et al. “The source counts of submillimetre galaxies detected at λ= 1.1 mm.” Monthly Notices of the Royal Astronomical Society, June 2012).

Developed a software package to determine the number density of galaxies from complex interferometric data and tested the algorithm through simulations.

Designed summer research projects and mentored two undergraduate students for the NSF Research Experiences for Undergraduates program.

Developed and tested an algorithm for calibrating astronomical interferometric data collected in band-to-band phase transfer mode.

Prepared Python-executable, publicly available data analysis scripts for science verification data taken with the ALMA telescope.

Maintained and improved the Splatalogue database for astronomical spectroscopy by updating information on atomic and molecular line transitions.

Organized informational workshops on the ALMA telescope nationwide and led presentations on telescope capabilities and hands-on data processing tutorials for the astronomy community.

Postdoctoral Researcher | September 2009 - July 2011

After completing my PhD, I pursued my first postdoctoral position at the University of Pennsylvania in the Department of Physics and Astronomy. This role allowed me to continue my research on using millimeter-wavelength surveys to study galaxy evolution in the early Universe, furthering my expertise in this specialized area of astronomy.

While at Penn, my achievements included:

Developed software to identify emission lines in the millimeter-wavelength spectra of galaxies to derive their cosmological redshifts.

Wrote simulation code to test algorithms for deriving cosmological redshifts of galaxies through low signal-to-noise detections of emission lines.

Modeled the emission line intensities of a galaxy using Monte Carlo Markov Chains to determine maximum likelihood values of its gas temperature and density.

Supported 500 hours of operations for the Z-Spec camera on two different telescopes, including planning and executing the data collection process.

Identified a mechanical problem with one of Z-Spec’s coupling mirrors through experimental trials and helped install and test the replacement mount.

Built the flux calibration model for the Z-Spec 2009 - 2010 observing season and implemented a process that improved the data quality by 5%.

Published a first-author paper in a peer-reviewed journal (K. S. Scott, et al. “Redshift determination and CO line excitation modeling for the multiply lensed galaxy HLSW-01.” The Astrophysical Journal, May 2011).

Graduate Student Researcher | June 2003 - September 2009

My journey into astronomy does not follow the traditional tale of childhood stargazing with a backyard telescope. Instead, it sprang from a love of math and physics. I found math to be an engaging language filled with precise rules and cool symbols. In contrast, physics intrigued me due to its universal applicability across scales and how it forces you to hold seemingly opposite beliefs in your mind at the same time. After completing my undergraduate degree and two summer internships in astronomy projects, I was drawn to pursue a PhD in radio astronomy, motivated by the allure of studying the unseen aspects of the Universe.

I was fortunate to join the PhD program in the Department of Astronomy at the University of Massachusetts, Amherst, which specialized in radio astronomy. As the first person in my family and social circle to enter graduate school, the experience was both challenging and transformative. Over those six years, I encountered rigorous coursework and adapted to an academic lifestyle while discovering the immense value of relationships, teamwork, and collaboration. These formative years honed my ability to work autonomously on creative and innovative projects. My research and dissertation demanded significant skills in computer programming and algorithm development, areas I discovered I truly enjoyed and that ultimately paved the way for a rewarding career in data science.

My key accomplishments during graduate school included:

Wrote code to identify peaks in large-area millimeter-wavelength images from the AzTEC camera, leading to the discovery of more than 1000 galaxies.

Used Bayesian methods to derive brightness probability distributions for millimeter-selected galaxies and determined their number density by bootstrap sampling.

Identified multi-wavelength counterparts from catalogs of more than 10,000 galaxies to objects detected at millimeter wavelengths by computing the likelihood of false association using p-value statistics.

Supported two months of operations for the AzTEC camera in Chile, including observing script generation, and planning and executing the data collection process.

Key developer of the publicly available AzTEC data-processing software; created algorithms for map-making from time-series data, atmosphere removal via principal component analysis, and optimal filtering using Fourier analysis.

Wrote software to combine time-synchronous data from the AzTEC camera and the JCMT telescope to optimize the output data quality.

Instructor for four semesters of Astronomy 103: Observational Astronomy; lectured about solar system bodies and led observations at the Orchard Hill Observatory.

Published two first-author papers in top peer-reviewed scientific journals:

K. S. Scott et al. “Deep 1.1mm-wavelength imaging of the GOODS-S field by AzTEC/ASTE - I. Source catalogue and number counts.” Monthly Notices of the Royal Astronomical Society, July 2010.

K.S. Scott et al. “AzTEC millimetre survey of the COSMOS field - I. Data reduction and source catalogue.” Monthly Notices of the Royal Astronomical Society, April 2008.