Data Scientist

Roles and responsibilities

How You’ll Make An Impact / Responsibilities

• Develop advanced analytics solutions leveraging artificial intelligence and machine learning techniques for application along the value chain
• Work in cross-organizational, multi-disciplinary teams supporting application and implementation of new data science and AI/ML concepts
• Drive the roll-out of AI/ML across the company through knowledge sharing, mentoring, supporting technical trainings
• Be up to date on latest developments in data science and AI/ML principles and theories to help the business make truly data-driven decisions
• Collaborate in a partner ecosystem across industry and academia to drive research and leverage know-how in this field
• Document the developed methods and processes and present results to stakeholders across the organization on a regular basis
  
  

What You Bring / Skills, Capabilities

• More than 3 years’ work experience, ideally in the energy industry
• University degree in Computer Science, Mathematics, Physics, Engineering, or related field
• Excellent analytical skills and profound expertise in data science, machine learning, artificial intelligence methods
• Programming skills in Python and/or R, good knowledge in the application of statistical modelling, experience in deployment and cloud computing
• Strong customer focus and excellent interpersonal skills, open-minded and willing to learn
• Ability to effectively collaborate in multidisciplinary, multinational, global teams
• Fluent in written and spoken English, excellent communication and presentation skills
• Enthusiastic about data and analytics and driving the use of data across all parts of a business and ecosystem

Desired candidate profile

As a data scientist, you’ll need a combination of technical, analytical, and soft skills. Here are some key skills that are essential for the role:

1. Statistical Analysis: Strong understanding of statistics and probability to analyze data sets and draw conclusions.

2. Programming Skills: Proficiency in languages like Python, R, or SQL for data manipulation and analysis.

3. Data Visualization: Ability to create clear and informative visualizations using tools like Tableau, Power BI, or Matplotlib.

4. Machine Learning: Knowledge of machine learning algorithms and frameworks (e.g., scikit-learn, TensorFlow) to build predictive models.

5. Data Wrangling: Skills in cleaning and preparing data for analysis, dealing with missing values, and transforming data formats.

6. Domain Knowledge: Understanding the specific industry you’re working in (e.g., finance, healthcare) to provide context to your analyses.

7. Big Data Technologies: Familiarity with big data tools and frameworks (e.g., Hadoop, Spark) for processing large datasets.

8. Critical Thinking: Ability to approach problems logically and make data-driven decisions.

9. Communication Skills: Effectively conveying findings and insights to non-technical stakeholders through storytelling and clear presentations.

10. Collaboration: Working well with cross-functional teams, including engineers, product managers, and business analysts.

Developing these skills will help you succeed as a data scientist and make impactful contributions to your organization.