Experienced data and AI/ML professional
Aktualisiert am 09.01.2025
Profil
Freiberufler / Selbstständiger
Remote-Arbeit
Verfügbar ab: 06.01.2025
Verfügbar zu: 100%
davon vor Ort: 20%
Machine Learning
Künstliche Intelligenz
Deep Learning
Data Analyst
Data Engineer
Data Scientist
Big Data
SQL
Python
MS Excel
VBA
Apache Spark
Azure
AWS
Swedish
Muttersprache
English
Verhandlungssicher
Norwegian
Fortgeschritten
Danish
Fortgeschritten

Einsatzorte

Einsatzorte

Deutschland, Schweiz, Österreich
möglich

Projekte

Projekte

2 months
2024-06 - 2024-07

Delivered an emotion analysis of reviews

Data Scientist Python Microsoft Excel
Data Scientist
  • Delivered an emotion analysis of reviews for their client - an internationally known group within beauty and personal care. By performing this analysis it was possible to identify growth opportunities for certain categories, products and brands by looking at, e.g., emotion trends over time. 
  • The delivery consisted of data analysis conclusions, visualizations, and workshop material for the end-client to use. Web scraped reviews were emotion-classified by using the RoBERTa Emotion Base model (utilizing the same underlying architecture as in many generative AI models) and then the emotion scores were processed using common data processing tools such as Pandas, Numpy, SciPy. 
  • This has provided the client with a type of analysis that previously was not feasible, and it provides a new dimension to their customer insights.
Python Microsoft Excel
Global Strategy, Innovation and Thinking Partner
Remote, EU
2 years 1 month
2021-04 - 2023-04

Engineered end-to-end AI/ML batch solutions

Data Scientist Python R SQL ...
Data Scientist
  • Engineered end-to-end AI/ML batch solutions to provide the assortment team with actionable data for optimized decision-making. This involved assortment optimization, vendor negotiations, and tracking campaign success, contributing to annual cost savings exceeding 100 MSEK. It was supplemented with ad hoc data analysis tasks (using Python) and customized reports, metrics and data visualizations. Systematic reporting was typically done in Micro Strategy and ad hoc reporting by using Python libraries such as Matplotlib, Seaborn, Plotly/Dash, and sometimes MS Power BI.
  • Developed high-performance classification models (AUROC > 0.95) for identifying professional customer types (e.g., restaurants, cafés). This was done using Python and scikit-learn. The results were used for improved targeted promotions and assortment optimization.
  • Managed, refactored and improved a deep neural network regression model used to enhance the measurement of promotion effects by assessing variances in non-promotional sales. The model was built using TensorFlow (Python), deployed on AWS SageMaker, and orchestrated by using Apache
  • Airflow. The model resulted in significant improvements compared to the previous solution and also included continuous training and evaluation, and
  • enabled model versioning - which was a big improvement in the context of MLOps.
  • Orchestrated workflows and ETL pipelines. This included, e.g., SQL tasks (advanced stored procedures, defining tables, views, and triggers) in
  • combination with dbt, utilizing AWS S3 as an intermediate storage for model input/output, and integration with downstream systems such as Teradata
  • EDW and SAP CAR. Many of the SQL tasks ran against the Teradata EDW and resulted in persisting prepared features - similar to a feature store.
  • Integrated CI/CD pipelines (using Bitbucket Pipelines, Jenkins, git, SVN) to establish good quality of code and functionality prior to integration.
  • Led and created consumer decision trees (CDTs) as a data driven way of visualizing the customer purchase process in a specific product category which supports the category manager in deciding and optimizing the assortment. The CDTs were developed in Python using SciPy for hierarchical clusterings on a similarity linkage matrix. It was deployed as a semi-automatic solution.
  • Mentored a junior data scientist, actively contributing to team growth and knowledge transfer within the organization.
Python R SQL dbt git Apache Subversion (SVN) Bitbucket Pipelines Jenkins Docker Apache Airflow Apache Spark AWS Teradata EDW MicroStrategy Microsoft Power BI
Dagab Inköp & Logistik AB
Stockholm, Sweden
2 years 2 months
2019-03 - 2021-04

Experimented, developed, productionalized and performed operations

Data Scientist Python R SQL ...
Data Scientist
  • Experimented, developed, productionalized and performed operations and maintenance of AI/ML models for large scale transaction monitoring contributing significantly to the advancement of the bank?s AML/CTF capabilities. This was performed on the data lake by utilizing Apache Spark and Apache Hive (SQL), often in combination with Python libraries such as scikit-learn and PyOD.
  • Reviewed a TensorFlow use-case where semi-supervised GANs were used to generate synthetic ML/TF patterns that could be included in the transaction monitoring process. GANs are a powerful and flexible tool in the field of generative AI, capable of creating highly realistic synthetic data across various domains.
  • Created the bank?s 1st unsupervised anomaly detection model, integrating Know-Your Customer data. The solution was built using Python and Apache Spark, and was deployed and orchestrated using the Ab Initio platform.
  • Migration of use cases from on-premises to (Microsoft Azure) Databricks.
  • Evaluated tools that could streamline the future ways of working with AI and machine learning models. From early model deployments we learnt that we had to introduce new best practices if we were going to ship models at scale. A DevOps/MLOps resource was hired and together we developed and deployed a solution that enabled sharing of version controlled common code (e.g., modules, libraries).
  • Co-developed an anomaly detection model targeting transaction behavior in a popular instant money transfer service. It was developed in Python using scikit-learn, and it was deployed and orchestrated on the Databricks platform. The new solution led to substantial improvements compared to an old rule based solution.
  • Created customized data visualizations using common tools like Qlik Sense, Shiny, Plotly, Dash, Matplotlib, and Seaborn to support the data analysis and communication process.
  • Co-authored and published a paper, actively participating in the development, testing, and real-world application of a graph-based solution for detecting anomalous group behavior, addressing challenges with incomplete network information.
  • Guided and mentored a junior data scientist.
Python R SQL git Bitbucket Pipelines Jenkins Apache Hadoop Apache Hive Apache Spark Microsoft Azure Databricks Teradata EDW Qlik Sense
Swedbank AB
Stockholm, Sweden
7 months
2018-09 - 2019-03

Forecasting financial market movements on futures using observed news flow

Quantitative Researcher Python git
Quantitative Researcher
  • Forecasting financial market movements on futures using observed news flow
  • Thesis: on request
Python git
Lynx Asset Management AB
Stockholm, Sweden

Aus- und Weiterbildung

Aus- und Weiterbildung

04/2019:

Industrial Engineering and Management

M. Sc.

Umeå University, Umeå, Sweden


Key Focus:

  • Welcomed and mentored international students, fostering a supportive and inclusive environment within and beyond the university community.
  • Introduced children aged 10-12 to programming and digital creation as a volunteer at Kodcentrum.
  • Concept development of an air contamination measurement system on assignment of FOI, the Swedish Defence Research Agency.
  • Software development and enhancement of a space planning tool for Örnsköldsviks Municipality


Certifications:

  • AWS Certified Cloud Practitioner (CLF-C02)
  • Astronomer Certification for Apache Airflow Fundamentals
  • Microsoft Azure Data Scientist Associate (DP-100)
  • Microsoft Azure Fundamentals (AZ-900)

Kompetenzen

Kompetenzen

Top-Skills

Machine Learning Künstliche Intelligenz Deep Learning Data Analyst Data Engineer Data Scientist Big Data SQL Python MS Excel VBA Apache Spark Azure AWS

Produkte / Standards / Erfahrungen / Methoden

Machine Learning
Classification
Regression
Clustering
Anomaly Detection
Forecasting
Deep Learning
Natural Language Processing (NLP)
Large Language Models (LLM)
Data Engineering
Data Wrangling
Software Engineering
Project Management
Agile software development
Visualization
Communication and Presentation
Risk Management

Technical skills:

  • Python
  • R
  • JavaScript
  • HTML/CSS
  • SQL
  • git
  • SVN
  • Apache Spark
  • Apache Hadoop
  • Apache Hive
  • Apache Airflow
  • CI/CD
  • Amazon Web Services (AWS)
  • Microsoft Azure
  • Google Cloud Platform (GCP)
  • Docker

Branchen

Branchen

Financial services

Fast-moving consumer goods (FMCG)

Wholesale

Retail

Einsatzorte

Einsatzorte

Deutschland, Schweiz, Österreich
möglich

Projekte

Projekte

2 months
2024-06 - 2024-07

Delivered an emotion analysis of reviews

Data Scientist Python Microsoft Excel
Data Scientist
  • Delivered an emotion analysis of reviews for their client - an internationally known group within beauty and personal care. By performing this analysis it was possible to identify growth opportunities for certain categories, products and brands by looking at, e.g., emotion trends over time. 
  • The delivery consisted of data analysis conclusions, visualizations, and workshop material for the end-client to use. Web scraped reviews were emotion-classified by using the RoBERTa Emotion Base model (utilizing the same underlying architecture as in many generative AI models) and then the emotion scores were processed using common data processing tools such as Pandas, Numpy, SciPy. 
  • This has provided the client with a type of analysis that previously was not feasible, and it provides a new dimension to their customer insights.
Python Microsoft Excel
Global Strategy, Innovation and Thinking Partner
Remote, EU
2 years 1 month
2021-04 - 2023-04

Engineered end-to-end AI/ML batch solutions

Data Scientist Python R SQL ...
Data Scientist
  • Engineered end-to-end AI/ML batch solutions to provide the assortment team with actionable data for optimized decision-making. This involved assortment optimization, vendor negotiations, and tracking campaign success, contributing to annual cost savings exceeding 100 MSEK. It was supplemented with ad hoc data analysis tasks (using Python) and customized reports, metrics and data visualizations. Systematic reporting was typically done in Micro Strategy and ad hoc reporting by using Python libraries such as Matplotlib, Seaborn, Plotly/Dash, and sometimes MS Power BI.
  • Developed high-performance classification models (AUROC > 0.95) for identifying professional customer types (e.g., restaurants, cafés). This was done using Python and scikit-learn. The results were used for improved targeted promotions and assortment optimization.
  • Managed, refactored and improved a deep neural network regression model used to enhance the measurement of promotion effects by assessing variances in non-promotional sales. The model was built using TensorFlow (Python), deployed on AWS SageMaker, and orchestrated by using Apache
  • Airflow. The model resulted in significant improvements compared to the previous solution and also included continuous training and evaluation, and
  • enabled model versioning - which was a big improvement in the context of MLOps.
  • Orchestrated workflows and ETL pipelines. This included, e.g., SQL tasks (advanced stored procedures, defining tables, views, and triggers) in
  • combination with dbt, utilizing AWS S3 as an intermediate storage for model input/output, and integration with downstream systems such as Teradata
  • EDW and SAP CAR. Many of the SQL tasks ran against the Teradata EDW and resulted in persisting prepared features - similar to a feature store.
  • Integrated CI/CD pipelines (using Bitbucket Pipelines, Jenkins, git, SVN) to establish good quality of code and functionality prior to integration.
  • Led and created consumer decision trees (CDTs) as a data driven way of visualizing the customer purchase process in a specific product category which supports the category manager in deciding and optimizing the assortment. The CDTs were developed in Python using SciPy for hierarchical clusterings on a similarity linkage matrix. It was deployed as a semi-automatic solution.
  • Mentored a junior data scientist, actively contributing to team growth and knowledge transfer within the organization.
Python R SQL dbt git Apache Subversion (SVN) Bitbucket Pipelines Jenkins Docker Apache Airflow Apache Spark AWS Teradata EDW MicroStrategy Microsoft Power BI
Dagab Inköp & Logistik AB
Stockholm, Sweden
2 years 2 months
2019-03 - 2021-04

Experimented, developed, productionalized and performed operations

Data Scientist Python R SQL ...
Data Scientist
  • Experimented, developed, productionalized and performed operations and maintenance of AI/ML models for large scale transaction monitoring contributing significantly to the advancement of the bank?s AML/CTF capabilities. This was performed on the data lake by utilizing Apache Spark and Apache Hive (SQL), often in combination with Python libraries such as scikit-learn and PyOD.
  • Reviewed a TensorFlow use-case where semi-supervised GANs were used to generate synthetic ML/TF patterns that could be included in the transaction monitoring process. GANs are a powerful and flexible tool in the field of generative AI, capable of creating highly realistic synthetic data across various domains.
  • Created the bank?s 1st unsupervised anomaly detection model, integrating Know-Your Customer data. The solution was built using Python and Apache Spark, and was deployed and orchestrated using the Ab Initio platform.
  • Migration of use cases from on-premises to (Microsoft Azure) Databricks.
  • Evaluated tools that could streamline the future ways of working with AI and machine learning models. From early model deployments we learnt that we had to introduce new best practices if we were going to ship models at scale. A DevOps/MLOps resource was hired and together we developed and deployed a solution that enabled sharing of version controlled common code (e.g., modules, libraries).
  • Co-developed an anomaly detection model targeting transaction behavior in a popular instant money transfer service. It was developed in Python using scikit-learn, and it was deployed and orchestrated on the Databricks platform. The new solution led to substantial improvements compared to an old rule based solution.
  • Created customized data visualizations using common tools like Qlik Sense, Shiny, Plotly, Dash, Matplotlib, and Seaborn to support the data analysis and communication process.
  • Co-authored and published a paper, actively participating in the development, testing, and real-world application of a graph-based solution for detecting anomalous group behavior, addressing challenges with incomplete network information.
  • Guided and mentored a junior data scientist.
Python R SQL git Bitbucket Pipelines Jenkins Apache Hadoop Apache Hive Apache Spark Microsoft Azure Databricks Teradata EDW Qlik Sense
Swedbank AB
Stockholm, Sweden
7 months
2018-09 - 2019-03

Forecasting financial market movements on futures using observed news flow

Quantitative Researcher Python git
Quantitative Researcher
  • Forecasting financial market movements on futures using observed news flow
  • Thesis: on request
Python git
Lynx Asset Management AB
Stockholm, Sweden

Aus- und Weiterbildung

Aus- und Weiterbildung

04/2019:

Industrial Engineering and Management

M. Sc.

Umeå University, Umeå, Sweden


Key Focus:

  • Welcomed and mentored international students, fostering a supportive and inclusive environment within and beyond the university community.
  • Introduced children aged 10-12 to programming and digital creation as a volunteer at Kodcentrum.
  • Concept development of an air contamination measurement system on assignment of FOI, the Swedish Defence Research Agency.
  • Software development and enhancement of a space planning tool for Örnsköldsviks Municipality


Certifications:

  • AWS Certified Cloud Practitioner (CLF-C02)
  • Astronomer Certification for Apache Airflow Fundamentals
  • Microsoft Azure Data Scientist Associate (DP-100)
  • Microsoft Azure Fundamentals (AZ-900)

Kompetenzen

Kompetenzen

Top-Skills

Machine Learning Künstliche Intelligenz Deep Learning Data Analyst Data Engineer Data Scientist Big Data SQL Python MS Excel VBA Apache Spark Azure AWS

Produkte / Standards / Erfahrungen / Methoden

Machine Learning
Classification
Regression
Clustering
Anomaly Detection
Forecasting
Deep Learning
Natural Language Processing (NLP)
Large Language Models (LLM)
Data Engineering
Data Wrangling
Software Engineering
Project Management
Agile software development
Visualization
Communication and Presentation
Risk Management

Technical skills:

  • Python
  • R
  • JavaScript
  • HTML/CSS
  • SQL
  • git
  • SVN
  • Apache Spark
  • Apache Hadoop
  • Apache Hive
  • Apache Airflow
  • CI/CD
  • Amazon Web Services (AWS)
  • Microsoft Azure
  • Google Cloud Platform (GCP)
  • Docker

Branchen

Branchen

Financial services

Fast-moving consumer goods (FMCG)

Wholesale

Retail

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