Raphael Mankopf

Tech Lead | Project Manager | Tech Due Diligence | Data Science | Data Architecture | Blockchain Technology | Web3 | Digital Infrastructure

Zürich, Switzerland


Experienced Tech Lead, Data Scientist and Interim CTO for Software Companies with focus on AI and Machine Learning. Building up on +7 Years of experience working with data, I support my clients with technical project management, data modelling, machine learning implementations, cloud data architecture design and end-to-end software development in the field of AI. I support Technical Due Diligence and Data Value Assessment during merge and acquisition process.

Domain experience

Relevant Total Experience: 7 years

Seniority Level: Manager

Sector Experience: 

Alternative Finance Asset Management Banks Consumer Lending Data and Analytics Diversified Financials DLT and Cryptocurrencies Energy Health Care Identity and Security Information Technology Payments Personal Finance and Planning Risk and Compliance


Skilled in: Corporate Strategy, Post Merger Integration (PMI), Joint Venture, Due Diligence, Financial Planning & Analysis (FP&A), Buy-and-Build, Value Creation & Enhancement, Fundraising strategy, Financial Modeling, Company Valuation

Technical Skills: Software as a Service (SaaS), JavaScript, Google Analytics, SPSS, VBA, Bloomberg Terminal, SQL, R, Microsoft Excel, Databases, PowerPoint, SAP, Stata, Java, Bloomberg, Matlab, Microsoft Office, Blockchain, Microsoft SQL Server, Trading Systems, Tableau, Python

Languages: German, Spanish, English

Work Experience

Roland Berger

Project Manager Data Science & Blockchain Technology



- 1/12/2023

- Valuation and Due Diligence of the leading online platform for children & family health in DACH - Technical assessment & Due Diligence of a UAE based MVNO provider - assessing the full technical architecture and data landscape for future readiness and fintech use case application - Digital Asset Strategy for Banks, Funds and other Financial Institutions / Corporations - Web3 & Crypto Economy, Mining technology & Proof of work / Proof of stake - Crypto Exchange Infrastructure, Liquidity Management, Market Making - Institutional Crypto Custody Technology, MPC / MultiSig, Cold & Hot Storage - Quantitative Finance, Algorithmic trading architecture & technology - Algorithmic forecasting, Quant Trading, Risk Management - Analytics & Data Strategy, Cloud Architecture, Tech & Data Due Diligence

Capgemini Invent

Project Manager Data Science & Analytics



- 1/1/2021

- FIX API Kubernetes Integration for real time trading applications - Lead a team of 15 data scientists and engineers in developing a market monitoring platform based on news data (e.g., Bloomberg, Reuters) and sentiment analysis for market surveillance in the pharmaceutical industry. - Develop machine learning application for predictive maintenance to reduce data quality issues by connecting multiple data warehouses of a global investment bank (Python / Pyspark / Hadoop).

Capgemini Invent

Senior Data Scientist



- 1/7/2020

- DevOps data engineering on AWS EKS, EC2, Lambda, Redshift for deploying solutions in cloud (e.g. data lake) - Natural language processing for text classification by applying unsupervised algorithms to streamline the processing of text queries for financial products (Python) - Develop dee-learning based anomaly detection model to allocate sensor errors and wrong data points via an autoencoder model (Pyspark, TensorFlow, Keras).

Capgemini Invent

Data Scientist



- 1/1/2019

- Develop dee-learning based anomaly detection model to allocate sensor errors and wrong data points via an autoencoder model (Pyspark, TensorFlow, Keras). - Statistical trend modelling to determine next vehicle purchase point and machine learning based classification to suggest most suitable vehicle configuration including analytics dashboards in Tableau & PowerBI - Helped a premium OEM to enrich B2B customer data base with industry type via web scraping. Use machine learning algorithms to predict typical vehicle configurations per industry type and to understand industry specific purchasing preferences. Pilot testing with dealership for demand prediction dashboard to optimize the build-to-stock model.


Stockholm School of Economics




- 1/1/2017

CEMS - The Global Alliance in Management Education




- 1/7/2016

Maastricht University

Finance & Economics



- 1/7/2014

Universidad del Pacífico (PE)

Finance & Economics



- 1/7/2014

Licenses / Certificates


Structuring Machine Learning Projects




Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization




Neural Networks and Deep Learning




Applied Machine Learning in Python




Machine Learning



Coursera Course Certificates

R Programming



Want to work with Raphael?

Sign up on the Fintalent platform to get in touch with Raphael and 2,500+ other M&A freelancers!