Data Scientist
Job description
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, and supporting technical trainings.
- Be up to date on the 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, and artificial intelligence methods.
- Programming skills in Python and/or R, good knowledge in the application of statistical modeling, 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:
Statistical Analysis: Strong understanding of statistics and probability to analyze data sets and draw conclusions.
Programming Skills: Proficiency in languages like Python, R, or SQL for data manipulation and analysis.
Data Visualization: Ability to create clear and informative visualizations using tools like Tableau, Power BI, or Matplotlib.
Machine Learning: Knowledge of machine learning algorithms and frameworks (e.g., scikit-learn, TensorFlow) to build predictive models.
Data Wrangling: Skills in cleaning and preparing data for analysis, dealing with missing values, and transforming data formats.
Domain Knowledge: Understanding the specific industry you’re working in (e.g., finance, healthcare) to provide context to your analyses.
Big Data Technologies: Familiarity with big data tools and frameworks (e.g., Hadoop, Spark) for processing large datasets.
Critical Thinking: Ability to approach problems logically and make data-driven decisions.
Communication Skills: Effectively conveying findings and insights to non-technical stakeholders through storytelling and clear presentations.
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.