Senior Data Scientist

• Proven experience (typically 5+ years) working as a Data Scientist, with a strong track record of successfully delivering data science projects from problem definition to deployment and impact measurement.
• Solid understanding of machine learning algorithms, statistical modeling, and data analysis techniques.
• Programming proficiency in Python or other programming language and working experience with its data science libraries. (e.g. scikit-learn, pandas, numpy)
• Excellent analytical and problem-solving skills with the ability to translate business needs into technical solutions.
• Strong communication and collaboration skills, with the ability to work effectively in a team environment.
• Fluency in English (both written and spoken).

Nice to Have:

• Awareness of the competitive landscape within the AutoML market, including understanding the strengths and weaknesses of various platforms and approaches.
• Hands-on experience using one or more AutoML platforms (e.g., Dataiku, Databricks, AWS Sagemaker, DataRobot, Google Cloud AI Platform, Azure Machine Learning etc).
• Experience with user interface (UI) and user experience (UX) principles and a user-centric approach to problem-solving.
• Familiarity with software development workflows and agile methodologies.
• Experience with cloud computing platforms (e.g., AWS, Azure, GCP).

About dotData

dotData is revolutionizing the way enterprises leverage their data with our innovative AutoML platform. We empower data scientists and business users alike to accelerate the data science process, uncover hidden insights, and drive significant business value. Our cutting-edge technology automates feature engineering, model building, and deployment, allowing teams to focus on strategic initiatives and faster time-to-value. dotData Poland plays a crucial role in our global innovation and product development efforts.

The Opportunity

dotData Poland is seeking a highly motivated and experienced Senior Data Scientist to join our dynamic team in Warsaw. In this role, you will be instrumental in shaping the future of our flagship product, dotData Enterprise Studio – our new intuitive interface for our powerful AutoML platform. You will collaborate closely with our Product and Engineering teams to ensure the usability, quality, and impact of new features, as well as contribute directly to the advancement of our core machine learning components. This is an exciting opportunity to make a significant impact on a leading-edge product in the rapidly evolving AutoML landscape.

Responsibilities

• Feature Validation and Feedback: Collaborate closely with Product Managers and Engineers to thoroughly validate the importance, usability, and quality of new features within dotData Enterprise Studio. Provide critical feedback on product functions from a field perspective, anticipating potential product issues. This includes designing and executing experiments, analyzing user workflows, and providing constructive feedback.
• Domain Expertise and Engineering Alignment: Engage in deep technical domain discussions with Engineering team members, demonstrating sufficient background knowledge to contribute meaningfully to the advancement of the platform.
• Prototyping and Innovation: Proactively suggest and prototype improvements and enhancements to the underlying machine learning components of the dotData platform. This may involve exploring new algorithms, techniques, or methodologies to enhance model performance, explainability, or efficiency.
• Collaboration and Communication: Effectively communicate your findings, insights, and recommendations to both technical and non-technical audiences. Work collaboratively within cross-functional teams to ensure alignment and successful implementation of new features and improvements.
• Staying Current: Keep abreast of the latest advancements in machine learning, data science, and the AutoML market.

Qualifications

• Proven experience (typically 5+ years) working as a Data Scientist, with a strong track record of successfully delivering data science projects from problem definition to deployment and impact measurement.
• Solid understanding of machine learning algorithms, statistical modeling, and data analysis techniques.
• Programming proficiency in Python or other programming language and working experience with its data science libraries. (e.g. scikit-learn, pandas, numpy)
• Excellent analytical and problem-solving skills with the ability to translate business needs into technical solutions.
• Strong communication and collaboration skills, with the ability to work effectively in a team environment.
• Fluency in English (both written and spoken).

Nice to Have

• Awareness of the competitive landscape within the AutoML market, including understanding the strengths and weaknesses of various platforms and approaches.
• Hands-on experience using one or more AutoML platforms (e.g., Dataiku, Databricks, AWS Sagemaker, DataRobot, Google Cloud AI Platform, Azure Machine Learning etc).
• Experience with user interface (UI) and user experience (UX) principles and a user-centric approach to problem-solving.
• Familiarity with software development workflows and agile methodologies.
• Experience with cloud computing platforms (e.g., AWS, Azure, GCP).

,[Feature Validation and Feedback: Collaborate closely with Product Managers and Engineers to thoroughly validate the importance, usability, and quality of new features within dotData Enterprise Studio. Provide critical feedback on product functions from a field perspective, anticipating potential product issues. This includes designing and executing experiments, analyzing user workflows, and providing constructive feedback., Domain Expertise and Engineering Alignment: Engage in deep technical domain discussions with Engineering team members, demonstrating sufficient background knowledge to contribute meaningfully to the advancement of the platform., Prototyping and Innovation: Proactively suggest and prototype improvements and enhancements to the underlying machine learning components of the dotData platform. This may involve exploring new algorithms, techniques, or methodologies to enhance model performance, explainability, or efficiency., Collaboration and Communication: Effectively communicate your findings, insights, and recommendations to both technical and non-technical audiences. Work collaboratively within cross-functional teams to ensure alignment and successful implementation of new features and improvements., Staying Current: Keep abreast of the latest advancements in machine learning, data science, and the AutoML market.] Requirements: Machine learning, Data analysis, Data science, scikit-learn, pandas, NumPy, Databricks, AWS, Google cloud, Azure, Machine Learning, User Interface, UX, Cloud, GCP Tools: Jira, Confluence, GitHub, GIT. Additionally: Training budget, Free coffee, Bike parking, Shower, Free snacks, Modern office, Startup atmosphere, No dress code.

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