Soren v Solari, Algorithms Developer in Big Sky, MT, United States
Soren v Solari

Algorithms Developer in Big Sky, MT, United States

Member since January 24, 2019
Soren is a master of many skillsets. With a Ph.D. in integrative neuroscience, he has delivered algorithmic patents used today by companies like Nestlé and Nissan, done business development sales at the C-level, and built scalable AWS systems. Soren has written real-time back ends, React front ends, cognitive systems, and genomic data mining algorithms from scratch. Soren is a brilliant systems thinker who can solve any problem with a rare combination of communication, architecture, and code.
Soren is now available for hire


  • Simpa
    Healthcare, Amazon Web Services (AWS), Microservices, React, Python...
  • Oregon Health & Science University
    Python, Data Science, Machine Learning, Natural Language Processing (NLP)...
  • Pantera Capital
    Python, Redshift, ETL, Cryptocurrency, APIs, AWS, AWS Fargate, Containers...



Big Sky, MT, United States



Preferred Environment

Amazon Web Services (AWS), Gmail, Linux, MacOS, Slack, GitHub, WebStorm, PyCharm, AWS, React, Python

The most amazing...

...product I've built is a personalized health app, writing 100% of the Python back end in scalable AWS, 100% of the React front end, models, and everything.


  • CTO

    2015 - PRESENT
    • Invented and built a revolutionary personalized health application that combines medical records, nutrition, activity, and arbitrary data for users.
    • Developed complex new healthcare predictive models for personalized health, including recipe and activity recommendations and blood work analysis. This model facilitates a world-class understanding of public nutrition data.
    • Built a complete front-end in React using a modern approach of only functional components leveraging React hooks and including high-throughput websockets and back-end API integration.
    • Deployed and managed dozens of different microservices; worked with a scalable API, websockets, DNS, and more.
    • Wrote every line of the core Python microservices framework using ZeroMQ for RPC communication, discovery services, load balancing, logging, monitoring, tracing, and continuous CDI testing/deployment to AWS infrastructure.
    • Worked with microservices and deployments on cloud-based infrastructure in automated ways.
    • Developed web scraping technology for personal health records as well as scraping recipe information from online resources.
    • Built a PDF reader model from scratch to extract information from PDFs (no third-party tools at all) starting with binary PDF input.
    • Developed other core technologies around natural language processing, artificial intelligence, machine learning, and multiple other concepts integrating complex data in sophisticated ways.
    • Created a chatbot from scratch with no third-party tools featuring interactive conversation, conversation tracking over time, and simultaneously/interchangeable interactivity via SMS as well as web app chatbot (through websockets).
    Technologies: Healthcare, Amazon Web Services (AWS), Microservices, React, Python, Natural Language Processing (NLP), Azure Machine Learning, Artificial Intelligence (AI)
  • Data Scientist

    2020 - 2021
    Oregon Health & Science University
    • Developed NLP models and new data transformation pipelines on large amounts of text data (for NLP predictive modeling) to create predictive models for a rare disease (Amyloidosis).
    • Developed remote data pipelines in a HIPAA setting to process large volumes of healthcare data (from ~12 different patient tables).
    • Created new data processing methodologies to run on arbitrary text data (from different sources) on patients in order to provide the highest predictive power of those patients that are undiagnosed.
    • Created NLP predictive models to be deployed to predict real-world patients on the transformed vector spaces of healthcare data.
    Technologies: Python, Data Science, Machine Learning, Natural Language Processing (NLP), Healthcare, Predictive Modeling
  • Database DevOps Creator for Time-series

    2020 - 2021
    Pantera Capital
    • Understood the client's needs on time-series and developed a problem statement. Researched all available time-series databases and recommended a solution and architecture.
    • Designed a Redshift database architecture to have the fastest possible access times (<500 ms) for arbitrary time-series data. Data is basically arbitrarily scalable.
    • Designed a database capable of handling minute-level real-time time-series for predictive models trading cryptocurrencies.
    • Wrote all the Python code and developed a custom Python package to access the database as concurrent connections to improve throughput.
    • Dockerized all applications maintaining data and deployed them to AWS Fargate. Created one-line deployments to simplify ongoing improvements.
    • Created a public-facing API via AWS Fargate (auto-scaling) to allow read/write/delete access to the database configured to grant permissions to individual time-series giving API keys. This allowed the client to give limited access to others.
    Technologies: Python, Redshift, ETL, Cryptocurrency, APIs, AWS, AWS Fargate, Containers, Docker
  • Senior Analytics Lead

    2019 - 2020
    Ensemble Health Partners
    • Developed novel algorithms and computational infrastructure for predictive models applied to large hospital outsourcing company resulting in $0.1 - $1.5 million in revenue lift per month per hospital (applied to dozens of hospitals).
    • Developed a novel algorithm to predict erroneous hospital inpatient visit records related to ICD-10 and diagnostic related group coding to ensure maximal profitability. The algorithm was deployed and is functional in dozens of hospital groups.
    • Developed novel algorithms applied to detect missing charges in outpatient hospital visits. Invented new algorithms related to association rules and k-nearest-neighbor to increase performance.
    • Established the computational infrastructure to rapidly build and deploy predictive models to dozens of health care clients. The infrastructure is now used by dozens of engineers to rapidly deploy models to all clients.
    • Developed an inpatient DRG encoder (from scratch) to augment predictive models. Required understanding the most inner workings of inpatient hospital billing.
    • Wrote patents and led several developers in the advancement of existing infrastructure on Azure.
    Technologies: Healthcare, Azure, SQL, Python, Natural Language Processing (NLP), Azure Machine Learning, Artificial Intelligence (AI)
  • Analytics Contractor

    2019 - 2019
    Department of Defense Subcontractor
    • Built and trained a lipreading deep neural network with TensorFlow to predict numbers, letters, and words that were spoken by readers.
    • Located and cleaned multiple training datasets for lipreading.
    • Configured and set up a GPU training pipeline on an AWS EC2 infrastructure.
    • Modified deep neural network construction and data pipelines to optimize for the real-world problem versus the academic problem initially posed by the client.
    • Achieved the client's desired 85% requirement for accuracy.
    Technologies: Amazon Web Services (AWS), GPU Computing, AWS, Python 3, Python, TensorFlow
  • Head of Analytics and Solution Architect

    2013 - 2015
    Nestlé Institute of Health Sciences
    • Invented and developed new nutrition analytics underlying a Nestlé/Samsung partnership. Built a nutrition recommendation engine leveraged largely inside Nestlé.
    • Developed and designed the core analytic infrastructure for a large-scale research institute, leading a team to implement it.
    • Developed bioinformatic models to integrate (genomics, proteomics, metabolomics, clinical data) for multi-million dollar clinical studies.
    • Led a team of six in analytics and infrastructure to build out data pipelines and analytic infrastructure, combining six data output from different research labs.
    Technologies: Natural Language Processing (NLP), Azure Machine Learning, Artificial Intelligence (AI), SQL, Microservices, Python
  • CTO | CEO

    2011 - 2013
    • Contributed to the build of neuroanatomically-based systems (brains in computers) along with the relevant infrastructure.
    • Created the first issued patent on simulated intelligence and neuroanatomically based systems.
    • Built full-scale brain simulations that were precursors to many of the neural net architectures used today.
    • Won support for early stage IARPA investment in DC.
    Technologies: Deep Learning, Computer Vision, Natural Language Processing (NLP), Azure Machine Learning, Artificial Intelligence (AI), React, Python
  • Senior Analytics Manager

    2009 - 2013
    Opera Solutions
    • Designed custom models (linear and non-linear) for multiple Fortune 1000 companies; this involved rewards recommendations, medical hospital visit revenue predictions, vehicle auction models, and more.
    • Invented a new type of generalized predictive adaptive non-linear model combining a Kalman filter with K-NN, and built and deployed the model to production. It requires near-zero maintenance with continuous best-in-class predictions.
    • Worked as a solution architect to both understand and formulate problems as well as develop rapid prototypes on the initial data.
    Technologies: Python


  • Cognitive Consilience

    Based on my PhD research, I developed the most comprehensive blueprint of neuroanatomical connectivity in the primate brain with a hypothesis concerning the functions of all the major structures in the brain.

    This may be the first scientific publication published simultaneously with an interactive web app, iPhone, and iPad app.

    An example of solving a fairly difficult problem: "How does the brain work?"


  • Languages

    Python, Python 3, SQL, C
  • Paradigms

    Microservices, Data Science, Microservices Architecture, Parallel Programming, ETL
  • Other

    AWS, Artificial General Intelligence (AGI), Machine Learning, Algorithms, Artificial Intelligence (AI), Analytics, Big Data, Natural Language Processing (NLP), Deep Learning, Computer Vision, Build Pipelines, Electronic Medical Records (EMR), Time Series, Mobile First, mobx state tree, Containers, Container Orchestration, OCR, APIs, Gmail, GPU Computing, HL7, Electrical Engineering, Cryptocurrency, Predictive Modeling
  • Frameworks

  • Libraries/APIs

    React, MobX, NumPy, TensorFlow, Python Asyncio, PyTorch
  • Tools

    GitHub, AWS Fargate, AWS Rekognition, AWS ECS, PyCharm, WebStorm, Slack, Azure Machine Learning
  • Platforms

    Docker, AWS EC2, MacOS, Linux, Amazon Web Services (AWS), Azure
  • Storage

    Data Pipelines, Redshift
  • Industry Expertise



  • PhD degree in Integrative Neuroscience
    2005 - 2009
    UCSD | University of California, San Diego - San Diego, CA, USA
  • Master's degree in Control Theory
    2002 - 2005
    UCSD | University of California, San Diego - San Diego, CA, USA
  • Dual bachelor's degrees (BSc/BA) in Electrical Engineering
    1995 - 1999
    University of San Diego - San Diego, CA, USA

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