Data Scientist

Responsibilities

• Acquire, clean, and process messy, real-world data from various sources to prepare it for analysis and modeling.
• Perform rigorous Exploratory Data Analysis (EDA) to understand data characteristics, identify patterns, uncover hidden insights, and formulate hypotheses.
• Translate complex business questions and challenges into well-defined data science problems and analytical tasks.
• Develop, train, and evaluate statistical and machine learning models to address specific business needs (e.g., prediction, classification, clustering, forecasting).
• Collaborate closely with engineering and product teams to deploy models into production environments, ensuring scalability, reliability, and performance monitoring.
• Communicate findings and model results clearly and effectively to technical and non-technical stakeholders, translating complex data analysis results into actionable business recommendations and solutions.
• Iterate on models and approaches based on performance feedback and evolving business requirements.
• Stay up to date with the latest advancements in data science, machine learning, and relevant technologies.

Experience Required

• Experience with data manipulation and analysis libraries/tools (e.g., Pandas, SQL).
• Demonstrated ability to deal with and process data from real-world sources.
• Experience performing Exploratory Data Analysis (EDA).
• Experience with model development (statistical modeling, machine learning).
• Familiarity with the process of deploying models into production or working alongside teams that do.
• Proven ability to translate business questions into data-driven problems.
• Ability to translate data analysis results and model insights into clear, business-oriented solutions.
• Proficiency in at least one major programming language used in data science (e.g., Python, R).

Experience Preferred

• Experience in Auto Industry
• Experience working with cloud computing platforms, particularly Google Cloud Platform (GCP).
• Experience with specific machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch).

Education Required

• Master's degree in Statistics, Mathematics, Computer Science, or a closely related quantitative field.

Education Preferred

• PhD in Statistics, Mathematics, Computer Science, or a closely related quantitative field