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Über
In this position, you will analyze data across the organization, with a particular focus on technical and deal diligence that supports the business development of our portfolio companies. You'll translate complex technical concepts into clear, actionable insights, enabling stakeholders to make informed decisions. As a Principal Data Scientist, you will lead efforts to design, build, and integrate innovative digital applications that align with our strategic and sustainable growth objectives.
This role goes beyond traditional data science; it is about using data to deliver tangible business outcomes. Collaborating with a larger offshore team, you will develop solutions that are not just theoretically sound but are pragmatically designed to drive change and deliver real-world results. Reporting to the Chief Data Scientist, you will establish yourself as a key figure in shaping our data-driven future, with a clear focus on practical applications and business impact.
Specific responsibilities will include (but are not limited to): Lead projects and collaborate with cross-functional teams, including data engineers, data analysts, software engineers, project managers and other stakeholders. Develop and deploy modern machine learning, statistical, ensemble, and generative AI methods to solve complex business and operational problems. Perform rigorous exploratory data analysis, including feature engineering, selection, and dimensionality reduction using statistical techniques. Use statistical methods for feature computation, selection, and dimensionality reduction. Creates effective visualizations for technical, business, and managerial audience. Effectively communicate results, insights, model interpretations, and their business implications to executive management and other senior stakeholders. Contributes to the company's digital strategic direction, development, and future growth. Drive the evaluation and adoption of AI and generative AI technologies through hands-on research, proofs of concept, and case studies. Critical thinking in building and deploying digital solutions. Effective change leader, skilled at resolving implementation hurdles and motivating adoption across the organization.
Required qualifications
BS, MS, or PhD in Computer Science, Applied Science, or an Engineering discipline (advanced degree preferred). Strong quantitative foundation in probability, statistics, algorithms, optimization, and machine/deep learning, with solid experience in classical and deep learning methods. Proven track record of delivering tangible business value using AI in real-world applications. Hands-on experience with modern ML frameworks such as Scikit-learn, TensorFlow, PyTorch, and Keras. Extensive programming experience in Python and SQL (with the ability to work in additional languages as needed). Demonstrated experience in time series analysis. Comfortable building data visualization solutions using tools such as Power BI, Spotfire, Bokeh, and Plotly (or similar). Fluency with big data and cloud ML platforms (e.g., Databricks, AzureML or similar). Familiarity with agile software development practices. Strong knowledge of MLOps principles and best practices.
Preferred qualifications
Deeper expertise in Bayesian methods and survival analysis. Hands-on experience with advanced generative models (e.g., GANs, VAEs, transformer-based models). Experience with GPU computing and parallel processing. Strong understanding of natural language modeling and practical experience in text analytics. Experience working on prognostic health management (PHM) or predictive maintenance projects. Additional programming experience in R or Julia. Experience querying and managing data in NoSQL databases. Experience in the petroleum E&P industry or in energy transition and decarbonization.
The preferred candidate will possess all or most of the following characteristics:
Strong communication skills and the ability to effectively discuss models with other data scientists as well as business partners at the appropriate level of technical detail. Awareness of the ethical implications of AI, particularly in the context of generative models, and commitment to developing responsible AI solutions. Excellent attention to detail and accuracy Proven ability to work independently and as part of a team
Sprachkenntnisse
- English
Hinweis für Nutzer
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