Data Scientist in Erlangen bei Quest Global

Data Scientist (f/m/d) in Erlangen bei Quest Global

Quest Global

Quest Global is an organization at the forefront of innovation and one of the world’s fastest growing engineering services firms with deep domain knowledge and recognized expertise in the top OEMs across seven industries. We are a twenty-five-year-old company on a journey to becoming a centenary one, driven by aspiration, hunger and humility.

We are looking for humble geniuses, who believe that engineering has the potential to make the impossible, possible; innovators, who are not only inspired by technology and innovation, but also perpetually driven to design, develop, and test as a trusted partner for Fortune 500 customers.

As a team of remarkably diverse engineers, we recognize that what we are really engineering is a brighter future for us all. If you want to contribute to meaningful work and be part of an organization that truly believes when you win, we all win, and when you fail, we all learn, then we’re eager to hear from you.

The achievers and courageous challenge-crushers we seek, have the following characteristics and skills:

High-Level Role Description:
As a Data Scientist, you will play a pivotal role in transforming data into actionable insights that drive strategic decisions and innovations. Your expertise in data analysis, machine learning, and statistical modeling will empower our organization to extract valuable information from complex datasets. You will collaborate with cross-functional teams to address business challenges, develop predictive models, and uncover patterns that contribute to the advancement of our products and services.

Responsibilities:
Data Analysis: Conduct exploratory data analysis to identify trends, anomalies, and patterns within diverse datasets.
Machine Learning: Develop and implement machine learning models to solve complex problems, leveraging algorithms for classification, regression, clustering, and more.
Predictive Modeling: Build predictive models that forecast business outcomes, customer behavior, and market trends.
Feature Engineering: Engineer and select relevant features from data to enhance model accuracy and performance.
Data Visualization: Create clear and insightful data visualizations to communicate findings and insights to stakeholders.
Experimentation: Design and analyze experiments to test hypotheses and optimize processes.
Collaborative Problem Solving: Collaborate with domain experts, engineers, and business stakeholders to define data science projects and align them with business goals.
Data Preprocessing: Clean, transform, and preprocess raw data to make it suitable for analysis and modeling.
Model Evaluation: Evaluate model performance using appropriate metrics and iterate to improve accuracy and robustness.
Insights and Recommendations: Provide actionable insights and recommendations based on data analysis to support decision-making.
Continuous Learning: Stay current with industry trends, advancements, and emerging technologies in data science and machine learning.
Documentation: Document methodologies, findings, and processes to facilitate knowledge sharing and reproducibility.
Ethical Considerations: Ensure data privacy, security, and ethical considerations in all aspects of data science projects.

Expected Skills & Capabilities:
Statistical Analysis: Proficiency in statistical methods, hypothesis testing, and experimental design.
Machine Learning: Strong understanding of machine learning techniques, algorithms, and frameworks.
Programming: Proficiency in programming languages such as Python or R for data analysis and modeling.
Data Manipulation: Experience with data manipulation libraries (e.g., pandas, NumPy) and SQL for data querying.
Data Visualization: Ability to create meaningful data visualizations using tools like Matplotlib, Seaborn, or Tableau.
Problem Solving: Analytical mindset to approach complex problems and formulate innovative solutions.
Mathematical Foundation: Strong mathematical background in linear algebra, calculus, and probability theory.
Domain Knowledge: Familiarity with the specific domain or industry the data science projects will address.
Communication: Excellent communication skills to convey technical concepts to both technical and non-technical stakeholders.
Experimentation: Understanding of experimental design and A/B testing methodologies.
Ethical Awareness: Knowledge of data privacy, security, and ethical considerations in data handling and analysis.
Version Control: Familiarity with version control systems (e.g., Git) for collaboration and code management.