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My elevator pitch …
I am Lukas Tycho and three words describe my business: ‘Data to Strategy’. With the power of AI, I help startups and small businesses with their product, business and funding strategies. I set myself apart by going the extra mile to determine causal relationships rather than just correlation, resulting in strategies that ‘work in the real world’ and ‘not just on paper’. Understanding the financial difficulty that startups may face when utilizing quality consulting, I work together with my clients to find bespoke solutions including equity schemes, making me a partner for the future with ‘skin in the game’ and not just a consultant.
… and in more detail
As a former loan underwriter and head of FP&A at a SME lender, that was itself a start-up, I acquired the knowledge to understand the needs of a startup. As part of my role in FP&A I enabled the company to not only acquire funding but also, with the help of investment bankers, acquire multiple small businesses itself. During that time I also learned how start-ups can fail and thus understand the pit falls and struggles.
As a Data Scientist, I have extensive experience in AI, Product Strategy, Product Analytics and Marketing Analytics; I am a mathematician by heart (and by M.Sc). I specialize in Causal Inference, which establishes causal relationships rather than correlation. Examples include Experimentation (A/B testing), alterations of Machine Learning models, and adaptations of robotics behavior models. I taught Data Science at General Assembly, led Data Science teams at Instagram, Aetna/CVS and Disney Streaming, and as mentioned above helped to grow a startup..
My personal story…
Growing up in northern Germany I have always been interested in science. I was particularly drawn to Mathematics and Astrophysics. After finishing my M.Sc in Mathematics at the Freie Universitaet Berlin love led me to the US. Sometimes things don’t work out and so I did a road trip across the country. Two nights in NYC became 9 years of exhilarating excitement, personal growth, and professional growth. In 2021 I decided to utilize working for Disney and transferred to Los Angeles. Why? Again for love; I know I am a romantic. However, when I am asked why I moved I reply: ‘Halfway to retirement. Next stop: Arizona’.
What did I do in the past and why me?
Growing a Start Up
Being my first job out of university, I was not aware of the unique circumstances I ended up in. The CEO, who became my mentor, always said: ‘You should pay me. This is a free business degree’ and in retrospect he was definitely right. The CEO founded a financial institute that he sold only after a few years to Deutsche Bank for half a billion dollars and subsequently became a Managing Director at Deutsche. When he founded the SME lender after his time at Deutsche he not only brought with him his experience but also various other talents with deep experience in various fields. I started in Underwriting approving SME loans and then transitioned via the Litigation department to FP&A where I worked on business valuations and M&A with investment banking.
Creating a product at Disney+
I led a team of Data Scientists in Core Experimentation to develop a software that is able to evaluate experiments (conducted on Disney+ and Hulu) programmatically and automatically such that the Product teams are able to evaluate experiments without additional help. In addition, I collaborated with the Product Team that is building an in-house experimentation platform.
This not only included consulting wrt. typical Product work such as UX/UI but also the integration of the evaluation software we developed with the experimentation platform; allowing the platform to programmatically and automatically evaluate experiments, again without any need for additional technical support.
Teaching at General Assembly
One of my favorite jobs I have had was teaching at General Assembly. Teaching and discussing new concepts is my biggest passion. I was able to create my own Data Science curriculum, which turned into guidelines for future Data Science courses. The Data Science bootcamp consisted of daily classes from 9am to 5pm (with a lunch break) and was tailored towards non-analytics professionals. This included teaching beginners how to program in Python, helping them to understand statistics and enabling them to build their first Machine Learning models.
Causal Inference at Aetna/CVS
Initially hired by Aetna (CVS bought Aetna during my time), I led the Data Science team that worked on increasing medication adherence as prescribed. The rationale is that for people with chronic conditions (i.e. renal failure) adhering to their medication regimen would decrease the chance of hospitalization and thus reduce medical costs.
This included conducting Causal Inference to determine how adherence is causally linked to hospitalization rate and single out specific conditions to target. We worked with the Behavior Change Marketing team, conducting marketing esq. experiments, in order to drive behavior change. Some of the methods used were emails reminding patients to pick up their medication or gift cards/cash for adhering to the prescribed regimen.
Product Analytics at Instagram
I was an individual contributor and worked as a Product Data Scientist on the Explore page and Hashtag following. Each product area had an integrated Product team consisting of Engineering, Product Management, UX/UI, Research and Data Science. My contributions to the team were two fold. Creating a Product roadmap by opportunity sizing different proposals, defining core KPIs, and setting/predicting/tracking KPI goals each quarter. Additionally, I evaluated the experiments we conducted and participated in the go/no-go decision process.
