REFERENZEN

DATA SCIENCE (DS)

Branche: Retail - Unternehmensgröße: <4.000

Projektinhalt:

Development of reliable, robust and scalable machine learning systems and platforms, created 360° customer insights data platform that feeds data into several downstream systems including a DMP for online advertising, designed a privacy-preserving data science platform for marketing departments of 8 group companies to comply with changes in privacy laws and managed its implementation, developed solutions to create a common latent feature representation from heterogeneous internal and external data sources using novel deep learning techniques. Conducted internal “business development” to promote group wide use of services and data pools.

Technologien & Methodik:

Python, R, Hadoop, Spark, Pandas, Scikit-Learn, Keras, TensorFlow, PyMC3, R-Studio, PostgreSQL, MySQL, Vertica, Greenplum, Jupyter, Anaconda, Git, Jenkins

Branche: Gaming - Unternehmensgröße: <1.000

Projektinhalt:

Full life-cycle data science project management and machine learning system development. Professionalized development processes in start-up environment and replaced failure-prone systems with reliable and maintainable solutions, redesigned a marketing attribution system and increased accuracy through new predictive models and by connecting external data sources, helped to save significant affiliate marketing expenses by providing fraud management team with novel fraud detection algorithms, developed a robust and accurate model for customer lifetime value prediction that became part of the main Business Intelligence ETL pipeline and supported automated and manual decision making processes in several departments.

Technologien & Methodik:

Python, R, Hive, Pandas, Scikit-Learn, Keras, TensorFlow, PostgreSQL, MySQL

Branche: Pharma - Unternehmensgröße: <250

Projektinhalt:

Quantitative quality assessment of the company’s next generation sequencing (NGS) laboratory for in vitro diagnostic (IVD) accreditation, utilizing machine learning for collaboration projects and product innovation (eg, cancer subtype classication), designing a new data model, including genomic variants and cancer type taxonomy, as basis for research and products, conceptualizing and performing statistical analysis of high-dimensional observational data („big data“).

Technologien & Methodik:

R, Python, SQL, Matlab, Java, machine learning, text mining, statistical optimization

Branche: Telekommunikation - Unternehmensgröße: <9.000

Projektinhalt:

Geo-Location: Home location detection and occupational segments identification, Movement patterns for different segments, Visualization of geolocation and relevant information.

Technologien & Methodik:

Python (Pandas, Keras, nltk), Spark, R

Branche: Energiehandel - Unternehmensgröße: <750

Projektinhalt:

Modellierung von Finanzmarktrisiken.

Technologien & Methodik:

Databricks on Azure, Spark Scala, Monte-Carlo-Simulation

Branche: Haushaltsgeräte - Unternehmensgröße: <2.500

Projektinhalt:

Aufbau einer Big-Data Analytics Abteilung durch Use Cases: Churn Analysis, B2B-Sales Optimization, Predictive Maintenance, Data quality.

Technologien & Methodik:

AWS, Spark, Scala, Sagemaker, TensorFlow, Keras, Python, RNNs