Bardiya B.-Data Scientist & Machine Learning Engineer
Check rate
Experience
Data Scientist
Rewe Digital GmbH
Statistical Forecasting Algorithm
- Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
- Migration from R/On-premise to Python/Snowflake
- Productionalization on Snowflake in cooperation with data engineers & DevOps
Monitoring Dashboard
- Data engineering for preparation & provisioning of necessary data/resources on Snowflake
- Development & deployment of a Streamlit dashboard in Snowflake
ML-based Probabilistic Forecasting on Vertex AI
- Development of a ML-based probabilistic forecasting algorithm from scratch
- Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI
Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow
Data Scientist
Coditorei GmbH
- End-to-End Development of 2 Content Based Recommender Systems
- Creation & Development of Code with Gitlab
- Implementation of Unit Tests
- Deployment of Batch Recommendation into AWS SQL-Database
- Implementation of Gitlab CI/CD Pipeline
- Improvement of implemented recommender systems with modernized tech stack (Pincone vector-database, TigerGraph graph-database) as MVP
- Development & Deployment of a Streamlit Web-App for Visualization/Analysis of Recommendations & other Data Science solutions
- Some MLOps & Data Engineering Tasks:
- Creation of Tables and Provision of Data in AWS RDS & Postgres
- Creation of batch prediction jobs for recommender via Gitlab pipelines
- Deployment of Data Science Web-App via Docker and Gitlab pipelines
- Creation Data pipelines in Kedro and Gitlab pipelines
Tech Stack: Python, Jupyter, SQLAlchemy, AWS/SageMaker, Gitlab, Gitlab CI/CD, Kedro, Docker, AWS RDS, Pinecone Vector DB, TigerGraph (DB, GraphStudio, PyTigerGraph), GraphQL, Postgres
Data Science Consultant
Tech Mahindra GmbH
Data Scientist
Mediengruppe RTL Deutschland GmbH
- Statistical Analysis & Visualization of TV Ratings/Reaches
- Timeseries Clustering of TV-Reaches & User-Behaviour with Hidden Markov Models & Dynamic Time Warping Algorithms
- Development of a variety of ML-Algorithms in the context of ad break reach & effectiveness forecasting, including Data Mining, Feature Engineering, Training, Testing & (Bayesian) Hyperparameter Optimization
- Decision-Tree Based Forecasting on structure data based on CatBoost
- Deep-Learning & NLP Based Forecasting on structured data & contextualized document embeddings from BERT with neural network (MLPs, CNNs and coupled MLP/CNN models
- Deep-Learning, NLP & Computer Vision based forecasting on sequential image as well as text & audio data with neural networks (RNNs, LSTMs, GRUs, CNNs
- LSTM & Transformer Based Forecasting for reach predictions on timeseries data
- Web Application Development & Deployment with Streamlit for monitoring production models and data
- Web-Scraping Data for Model Development
- ETL Processes & Feature Engineering in Spark, Hive, SQL, BigQuery
- Consulting & Support of less experienced on their projects
Tech Stack: Python (Numpy, Pandas, SQLAlchemy), Jupyter Notebook, Tensorflow, Keras, PyTorch CatBoost, Spark, Hive, Hadoop, BigQuery, SQL, Streamlit, MLFlow, Google Cloud Platform, Docker
Market risk control at a large German bank
- Analysis of risk relevant figures (VaR, SVaR and sensitivity figures) and identification of driving factors (on risk factor level, as well as portfolio and trade level)
- Validation and reporting of the daily VaR and SvaR risk figures
- Monitoring risk limits and support of portfolio supervision in ensuring compliance with given limits
Web-Application Development
- Implementation of a RESTful API for the system integration, using the Java-framework Jersey
- Implementation of data-queries and mechanisms for processing data and generating accounting-relevant bookings, using SQL and the Java-framework Hibernate
- Implementation of hedge accounting reports and their presentation in a Web-GUI, using the Java-framework Primefaces
Consultant (Financial Sector)
d-fine GmbH
04/2018 – 05/2018
Training in a variety of topics in finance (e.g. price valuation of financial instruments, market risk, credit risk) and IT (see computer skills)
06/2018 – 08/2018
Web-Application Development: Development of a Java-based external (CC)IR hedge accounting module for the integration into a treasury management system
- Implementation of a RESTful API for the system integration, using the Java-framework Jersey
- Implementation of data-queries and mechanisms for processing data and generating accounting-relevant bookings, using SQL and the Java-framework Hibernate
- Implementation of hedge accounting reports and their presentation in a Web-GUI, using the Java-framework Primefaces
09/2018 – 12/2018
Market risk control at a large German bank:
- Analysis of risk relevant figures (VaR, SVaR and sensitivity figures) and identification of driving factors (on risk factor level, as well as portfolio and trade level)
- Validation and reporting of the daily VaR and SvaR risk figures
- Monitoring risk limits and support of portfolio supervision in ensuring compliance with given limits
Industry experience
See where this freelancer has spent most of their professional time.
Experienced in Information Technology, Advertising, Media and Entertainment, Retail, Professional Services, and Banking and Finance.
Business area experience
See which departments and functions this freelancer has contributed to most.
Experienced in Information Technology, Product Development, Business Intelligence, and Finance.
Summary
- Part of the Winning Team at the Bertelsmann Hackathon 2019
- Part of the Winning Team at the Bertelsmann HackDays 2019
- Scholarship for doctorate financed by HGS-HIRe (2013-2017)
Skills
Data Science
Machine Learning
Deep Learning
Bayesian & Causal Models
Python, Numpy, Pandas, Scikit-Learn, Tensorflow, Pytorch, Pymc3
Sql, Spark, Bigquery, Mongodb
Google Cloud Platform
Tigergraph & Gsql
Programming, Scripting & Querying Languages: Python, C/C++, Java, Oop, Sql, Spark, Gsql (Tigergraph), Graphql, Bash Scripting
Python Libraries: Pandas, Numpy, Matplotlib, Plotly, Streamlit, Scikit-Learn, Tensorflow, Keras, Pytorch
Data Science: Machine Learning & Deep Learning, Monte-Carlo Simulation, Bayesian & Causal Models, Graph Algorithms & Machine Learning, Markov Models & Hidden Markov Models
Tools: Unix/Linux Systems, Version Control Systems (Git, Svn), Jupyter Notebook, Vscode, Gcp (Vertexai, Kubeflow), Aws, Azure, Tigergraph, Dbt
Python Libraries: Mlflow, Plotly Dash, Flask, Fastapi, Scrapy
Cloud: Tigercloud, Ibm Cloud & Watson, Docker
Data Storage/Querying: Mongodb, Hive, Hdfs, Hadoop, Neo4j
Programming & Scripting: R, Mathematica, Matlab/Octave, Excel/Vba
Web (App) Development: Html, Css, Javascript, Jquery
Languages
Education
Justus-Liebig-Universität
PHD · Theoretical Particle Physics · Giessen, Germany
TU Darmstadt
Master of Science · Physics · Darmstadt, Germany
TU Darmstadt
Bachelor of Science · Physics · Darmstadt, Germany
Certifications & licenses
AWS Fundamentals: Going Cloud-Native
AWS
Azure Data Scientist Associate
Microsoft
Certified TigerGraph Associate
TigerGraph
Databases for Data Scientists
University of Colorado Boulder
Deep-Learning Specialization
deeplearning.ai
Functional Programming Principles in Scala
EPFL University
Generative Adversarial Networks
deeplearning.ai
Graph Algorithms for Machine Learning
TigerGraph
IBM AI Engineering Professional Certificate
IBM
IBM Professional Data Science Certificate
IBM
Introduction Level Training in R-Programming
Mediengruppe RTL Dtl. GmbH
Introduction to Computational Statistics with PyMC3
Databricks
Introduction to MongoDB
MongoDB
Machine Learning
Stanford University
Natural Language Processing
deeplearning.ai
Professional Machine Learning Engineer (in progress)
Google Cloud
Querying Data with Transact-SQL
Microsoft
Reinforcement Learning
University of Alberta
TensorFlow in Practice
deeplearning.ai
TensorFlow: Data & Deployment
deeplearning.ai
Trainings in Java, SQL & Web-App Development
d-fine GmbH
Statistics
Experience
Global experience
Expertise
Qualifications
Profile
Frequently asked questions
Have questions? Find more information here.
Bardiya is based in Frankfurt am Main, Germany.
Bardiya speaks the following languages: German (Native), English (Advanced), French (Advanced).
Bardiya has at least 8 years of experience. During this time, Bardiya has worked in at least 5 different roles and for 5 different companies. The average length of individual experience is 1 year and 2 months. Note that Bardiya may not have shared all experience and actually has more experience.
Based on recent experience, Bardiya would be well-suited for roles such as: Data Scientist, Data Science Consultant, Market risk control at a large German bank.
Bardiya's most recent position is Data Scientist at Rewe Digital GmbH.
In recent years, Bardiya has worked for Rewe Digital GmbH, Coditorei GmbH, Tech Mahindra GmbH, and Mediengruppe RTL Deutschland GmbH.
Bardiya is most experienced in industries like Information Technology, Advertising, and Media and Entertainment. Bardiya also has some experience in Retail, Banking and Finance, and Professional Services.
Bardiya is most experienced in business areas like Information Technology, Product Development, and Business Intelligence. Bardiya also has some experience in Finance and Accounting.
Bardiya has recently worked in industries like Information Technology, Advertising, and Media and Entertainment.
Bardiya has recently worked in business areas like Information Technology, Business Intelligence, and Product Development.
Bardiya holds a Doctorate in Theoretical Particle Physics from Justus-Liebig-Universität, a Master in Physics from TU Darmstadt, a Bachelor in Physics from TU Darmstadt and a Bachelor in Mathematics from TU Darmstadt.
Bardiya has 21 certificates. Among them, these include: AWS Fundamentals: Going Cloud-Native, Azure Data Scientist Associate, and Certified TigerGraph Associate.
Bardiya will be available full-time from November 2026.
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Calculated based on our freelancers’ daily rates as of 6 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
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