Data Science Machine Learning

Jenna Hoffman

**** **** ******, **. *******, MO *3303

Phone: 636-***-**** E-mail: **************@*****.*** Summary

Innovative, energetic, focused scientist excited about advanced analytics, complex data, integrated pipelines and leading teams to build novel data science solutions for critical business problems. I am focused on creating diverse, highly motivated research teams that can innovate and deliver quickly. Education

Ph.D. Genetics and Statistics (May 2012): Iowa State University Genomic Landscape of the Soybean Genome

BA English (May 2007): University of Iowa

Skills

● Helping organizations build their strategy, vision and roadmap during technology transformations

● People leadership and cross functional team leadership

● End to end data science team, environment and culture building

● Technology and process change adoption, strategy creation and vision building

● AI, machine learning and statistical techniques

● Collaboration leader (both internal and with external teams) focused on results

● Continuously innovating and exploring new technology to deliver better products Tools and environments: Python, R, QGIS, PySpark, DataBricks, SAS, AWS (familiar with most AWS open-source technologies for data architecture, pipelines, discovery and development, and deployment), Azure, Linux, Unix, and multiple others.

Experience

Ameren

Lead of Data Science (July 2022-Present)

• Led the conversations, process and integration of a data science strategy which focused on where the company wants to be in five years and integrated talent gaps and opportunities for reskilling, timelines and roadmap, process for working in cross-collaborative teams which includes bringing in the end users through every step of the process and developing the right executive leadership collaboration

• Build and develop a Data Science Committee that promotes and enables cross-functional collaboration and ownership across the organization

• Lead the first cross-functional Data Science and Data Engineering team to deliver the first data science products around smart meter analytics

• Deliver and sell a strategy for building a standardized environment, platform and process that enables reproducible, scaleable and shareable data science products

• Lead a team of data engineers, ML engineers, ML Ops and an SRE to build out a Model Store, Feature Store and Discovery environment

• Lead and train my teams and others on the importance of continuous training, integrated CI/CD and ML monitoring into our platform

• scores.

Corteva

Head of Marketing and Sales Data Science (September 2021-July 2022)

• Develop the strategy and vision for our commercial data science platform

• Lead the development of customer models (churn, prospect, CLV, brand personas)

• Bring together the data scientists across the globe to build a standardized environment, platform, and languages

• Discover, create, and implement new technologies and solutions for our commercial team

• Integrate data across platforms (AWS, GCP, Azure) to deliver real-time customer recommendations and recommendations to retailers

• Delivered an elastic pricing tool integrated with our demand forecasting algorithm to help with supply constraints

• Experience in PyTorch, TensorFlow, DataBricks, Azure, R, forecasting algorithms, neural networks, propensity scores.

UniGroup

Head of Data Science (May 2021-September 2021)

• Developed computer vision algorithm that customers could utilize within their homes to determine the estimated cost of moving the household items that they have and utilize this imagery and information for moving insurance purposes as well

• Worked closely with executive stakeholders to define the data strategy and vision for UniGroup which focused on capacity, logistics and leading towards data as a service for our co-op partners

• Establish best practices and implement a more robust data pipeline from ingestion to decision

• Lead a group focused on creating insights and solutions for the moving industry

• Provide mentorship, technical guidance, and support in developing, testing and validating data science solutions such as forecasting, computer vision models, neural networks and optimization algorithms.

• Work with external collaborators (AWS, Dougherty Group, Alation) to create and begin delivering a strategy for talent assessment, training and gap identification, define our standard tool and pipeline process for data, build in data governance and best practices

• Experience in SAS, TensorFlow, AWS, Glue, EC2. Atlation, R, Python, SQL Growers Edge

Chief Data Officer (September 2020-May 2021)

• Led the software engineers, UI/UX, testing, support, data engineers, data scientists and data security teams that were responsible for building applications that delivered data-driven, automated, quick crop insurance to farmers while also working on building benefits and opportunities for regenerative and sustainable farming options

• Explored and tested data science solutions to predict the historic yield potential of farms to enable faster, data-driven insurance policies to farm operations

• Brought in and led a Chief Security Officer to transform our data policies to ensure customer trust and drive customer privacy policies to ensure our customers trusted us with their data

• Created and iterated on the company vision by working closely with our board of directors as well as many direct customers and partners

• Drove growth in our data, software, security and UI/UX teams by bringing in the best talent and growing a team of 16 to 42

• Experience in AWS, Google Earth, Python, Big Query, Looker VF

Chief Data Scientist January 2020 – September 2020

• Built a product that utilized computer vision algorithms to detect similar styles and patterns of clothing to build recommendation algorithms for product placement on our websites.

• Unified a team of data scientists spread across the globe into a central community and organization to enable end-to-end product development, increase retention with training and cross-organizational opportunities, standardization of code and software products and align on our data vision

• Developed an end to end data product pipeline including engagement with the end-users and appropriate stakeholders to ensure trust, validation and process changes are moving at the same pace as the delivery of the product.

• Introduced neural networks to our customer personalization journey

• Prioritized and led the data governance community focused on standardizing data assets to enable data sharing and insight growth across the diverse brands

• Built an algorithm to simulate sales across the global brands to understand the potential impact and drive demand forecasting decisions based on the probability and risk of new waves of COVID occurring

• Experience in SAS, AWS, GCP, R, Python, DynamicDB while developing supply and demand forecasting algorithms, predicting customer value (churn, propensity, retention, loyalty, etc.), defining brand differentiation Bayer

Head of Phenomics May 2017 – January 2020

• Researched and worked with external vendors to determine the best sensors, platforms, off-the shelf analytic tools, integrated software for our imagery research projects which included RGB, Lidar, hyperspectral sensors

(among others) and included ground-walking robotic sensor platforms, drones and satellite platforms

• Built a Lidar product that utilized Lidar sensors on a drone that determined the plant height and health of our plants at a .1 cm resolution. We built the data capture, ingestion, cleansing, calibration, geo-referencing, image detection and plant health measurement algorithms from end to end, deployed it utilizing continuous deployment methodology, working closely with our field teams for appropriate testing of the technical pieces as well as testing integration into their workflow process.

• Built a team of five data scientists into a team of over forty data scientists, computer vision scientists, remote sensing scientists, environmental modelers, statisticians, phenomic scientists and physicists to innovate and be the leaders in phenomic science in the agriculture domain

• Developed and delivered a UAV pipeline that could deliver 0.5 cm resolution field insights. This pipeline was created with collaborations from AWS to enable connectivity in remote locations; with our IT team to help with automation, compute resources, dev ops and support; with our field teams for validation; and with our breeding and commercial teams on priority and delivering valuable insights

• Developed weather clusters that integrated genomic and phenomic historical insights and in-season data provided from drones, machinery and satellites to drive real-time operation prescriptions and enable simulation of testing environments which decreased the length of our product pipeline, improved risk calculations, optimized product prediction and uncovered gaps in our product line-up.

• Continuously refine and drive the strategy in an innovative and rapidly changing technology area

• Map out communication plans, process changes and champions for integration of disruptive technology

• Develop innovation strategy plan for smooth transition from discovery to production including definition of roles, integration of “champions”, definition of stakeholders and a standardized product development plan.

• Experience in QGIS, Python, AWS, DynamicDB, S3, EC2, Redshift, etc. DuPont Pioneer - June 2012 – May 2017

Data Science Program and Portfolio Manager January 2016 – May 2017

• Built a cross-functional team that covered imagery hardware (RGB, LIDAR, Hyperspectral, etc.), image analytics

(orthomosaic generation, attribution, feature extraction), data science (predictive models), data engineers

(ingestion, cleansing, storage), computational scientists (scaling algorithms) and deployment (support, validation)

• Responsible for image analytics and predictive agriculture solutions (innovation, development, deployment, and support)

• Lead team of thirty-four individuals consisting of data scientists, data engineers, computational scientists, image analysts, software developers and support

• Advise executives on resourcing and feasibility of proposed initiatives Page 3 [Type your e-mail address]

• In charge of developing and managing an environment that fosters innovation in the team but still focuses on delivering predictive agriculture solutions using modeling and machine learning

• Communication of vision, products and roadmap to leadership and to tactical teams

• Lead of the relationship between inventing scientists and technology drivers and the data science, data engineer and software development teams

• Scouting for new talent and external collaborations

• Establish end to end products from innovation to solution

• Coordinate and plan portfolio dependent on priorities and available resources

• Experience in AWS, Azure, Terradata, Hadoop, Pearl, R, Python, C, SQL Research Scientist (June 2014 –January 2016)

• Led a team of twelve individuals with skill sets of data engineers, data scientists, image analysts, drone hardware experts, and support

• Deployed innovative in-house orthomosaic algorithm

• Built feature extraction algorithm using machine learning for drone imagery

• Built predictive model using random forest and cross-validation algorithms to determine quality of the farmer’s field

• Built a cross-functional team that covered imagery hardware (drones, sensors), image analytics (orthomosaic generation, attribution, feature extraction), data science (predictive models), data engineers (ingestion, cleansing, storage), computational scientists (scaling algorithms) and deployment (support, validation) that scaled by 300% in one year

• Embedded agile practices within the programs including requirements planning, strategic process modeling, traceability, and quality management techniques

• Maintained cross-functional relationships and coordination across our teams (both internal and contract) Senior Research Associate (June 2012 –June 2014)

• Built a novel pipeline using Bayesian statistics to determine copy number for transgenic events that allowed scaling the number of events we created

• Evaluated use of neural networks to identify potential genes of interest

• Built an application that detected single nucleotide polymorphisms in transgenic events

• Business analyst and project lead for several programs with a mix of internal and contract teams

• Established best practices for scientific business analysts

• Used requirements and system analysis to dissect and plan complex scientific software solutions

• Effectively managed communication across a wide variety of groups

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