Kandideeri ITTM-i

www.haut.ai

Tallinn

Remote or hybrid work is possible.

• Clean and prepare data for model training
• Analyze and explore data
• Develop computer vision algorithms
• Write machine learning training pipelines
• Run machine learning pipelines
• Evaluate machine learning models
• Present modeling results
• Document model cards

Computer vision and deep learning for skincare

We want you to:

• Be able to write a clear code in Python (pandas, numpy, scikit-learn, matplotlib, plotly, opencv)
• Be familiar with at least one DeepLearning Framework
• Have good understanding of basic computer vision algorithms
• Have advanced English

Timur Tlyachev, Head of Data Science

I round

1. Who are you and what is your specialization?
2. What do you expect from the program when joining Haut.AI?
3. Describe your last/favorite project/homework related to data science and what part did you play in it?

II round

You can expect a virtual interview or face-to-face chat with coding exercises.

www.haut.ai

Tallinn

Remote or hybrid work is possible.

• Cleanse and prepare data for analysis
• Analyze and explore data
• Do ad-hoc reporting
• Develop analytical dashboards
• Interpret and analyze data using statistical techniques
• Prepare statistical reports
• Do data visualization
• Present results of analyses

Time-series analysis of skin conditions and consumer clustering based on skin conditions

We want you to:

• Be able to write a clear code in Python (pandas, numpy, scikit-learn, matplotlib, plotly)
• Have good knowledge of SQL (PostgreSQL)
• Feel strong in mathematical statistics
• Be familiar with basic unsupervised ML algorithms
• Be familiar with data visualization approaches
• Have advanced English

Timur Tlyachev, Head of Data Science

I round

1. Who are you and what is your specialization?
2. What do you expect from the program when joining Haut.AI?
3. Describe your last/favorite project/homework related to data science and what part did you play in it?

II round

You can expect a virtual interview or face-to-face chat with coding exercises.

https://medium.com/bolt-labs

Tartu, Tallinn

You'll be doing hybrid work.

• Participating in an iterative research process
• Formulating research questions
• Reading, presenting and reproducing previously published research related to the problem
• Creating, evaluating and documenting machine learning modelling approaches for improvement of text classification and semantic understanding tasks
• Maintaining reproducible research code

• Multilingual text classification
• Semantic text classification

• Good analytical thinking
• Experience in Python programming, including libraries such as Pandas and PyTorch
• Understanding of machine learning (ML) modelling approaches with focus on NLP; ML model evaluation
• Familiarity with modern NLP models, methods and packages: LLMs, transformers
• Proactive mindset, willingness to take initiative and getting things done

Maksim Butsenko, PhD, Staff Data Scientist

I round

1. What drew you to the specific topic you are applying for?
2. How do you see your work in this program contribute to your broader career goals, and how do you plan to leverage this experience to grow and develop professionally?
3. Can you describe a time when you worked collaboratively with a team to achieve a goal? What did you learn from the experience?
4. Describe a machine learning project you've worked on in the past. What were the challenges you faced, and how did you overcome them?

II round

We want to have a video interview with you.

https://medium.com/bolt-labs

Tartu, Tallinn

You'll be doing hybrid work.

• Collaborate on defining research questions and setting goals
• Reading, presenting, and reproducing previously published research related to the problem.
• Clear and transparent documentation of results
• Implementation of proof of concept (PoC)

• Always valid p-values for continuous monitoring experiments
• Variance reduction using ML

• Experience with Python and data related libraries, like Pandas.
• Familiarity with SQL
• Knowledge/interest in experimentation and causal inference
• Experience in ML modelling

• Carlos Bentes, Senior Data Scientist
• Garret O'Connell, Phd, Senior Data Scientist

I round

1. What drew you to the specific topic you are applying for?
2. How do you see your work in this program contribute to your broader career goals, and how do you plan to leverage this experience to grow and develop professionally?
3. Can you describe a time when you worked collaboratively with a team to achieve a goal? What did you learn from the experience?
4. Describe a machine learning project you've worked on in the past. What were the challenges you faced, and how did you overcome them?

II round

We want to have a video interview with you.

https://medium.com/bolt-labs

Tartu, Tallinn

You'll be doing hybrid work.

• Participating in an iterative research process
• Formulating research questions
• Reading, presenting and reproducing previously published research related to the problem
• Creating, evaluating and documenting approaches for improvement of data platform offering
• Maintaining reproducible research code

• Automation of solutions for data quality and performance issues
• Solving Data Governance with LLMs

• Experience with Python and data related libraries, like Pandas
• Experience with SQL
• Knowledge/interest in data platforms and data governance
• Proactive mindset, willingness to take initiative and getting things done
• Interest in LLMs

• Erik Heintare, Head of Analytics Platform
• Pawel Kupidura, Senior Software Engineer

I round

1. What drew you to the specific topic you are applying for?
2. How do you see your work in this program contribute to your broader career goals, and how do you plan to leverage this experience to grow and develop professionally?
3. Can you describe a time when you worked collaboratively with a team to achieve a goal? What did you learn from the experience?
4. Describe a machine learning project you've worked on in the past. What were the challenges you faced, and how did you overcome them?

II round

We want to have a video interview with you.

https://medium.com/bolt-labs

Tartu, Tallinn

You'll be doing hybrid work.

• Participating in an iterative research process.
• Reading and presenting previously published related research.
• Creating, evaluating and documenting reinforcement learning approaches for dispatching optimization.
• Creation of reproducible research code

Reinforcement learning for dispatching optimization

• Experience with Python
• Some familiarity with at least one deep learning framework (tensorflow, pytorch)
• Interest in optimization and reinforcement learning

Anatolii Prokhorchuk, PhD, Staff Data Scientist

I round

1. What drew you to the specific topic you are applying for?
2. How do you see your work in this program contribute to your broader career goals, and how do you plan to leverage this experience to grow and develop professionally?
3. Can you describe a time when you worked collaboratively with a team to achieve a goal? What did you learn from the experience?
4. Describe a machine learning project you've worked on in the past. What were the challenges you faced, and how did you overcome them?

II round

We want to have a video interview with you.

https://medium.com/bolt-labs

Tartu, Tallinn

You'll be doing hybrid work.

• Participating in an iterative research process
• Reading, presenting and reproducing previously published related research
• Creating, evaluating and documenting machine learning modelling approaches for traffic modelling
• Maintaining reproducible research code

Topics related to traffic modelling. For example, using machine learning approaches for traffic modelling using GPS probe data.

• Good analytical thinking
• Interest in traffic modelling
• Experience in Python programming, including libraries such as Pandas, sklearn, NumPy, (PyTorch/Tensorflow)
• Understanding of machine learning (ML) modelling approaches; ML algorithm classes such as boosting, neural networks; ML model evaluation
• Proactive mindset, willingness to take initiative and getting things done

Joonas Puura, Machine Learning Engineer (Geo Data Science)

I round

1. What drew you to the specific topic you are applying for?
2. How do you see your work in this program contribute to your broader career goals, and how do you plan to leverage this experience to grow and develop professionally?
3. Can you describe a time when you worked collaboratively with a team to achieve a goal? What did you learn from the experience?
4. Describe a machine learning project you've worked on in the past. What were the challenges you faced, and how did you overcome them?

II round

We want to have a video interview with you.