Data Scientist

About Us:
In Global Data Insight & Analytics (GDI&A), we harness the power of data and artificial intelligence to navigate Ford Motor Company through the disruptiveness of the information age. We're a team of innovators who strive to realize the enterprise's goals, reveal hidden opportunities, and achieve data superiority.

About You:

We are looking for a seasoned Data Scientist with 3+ years of experience in AI/ML, preferably with some experience in Generative AI. You're a humble and collaborative individual who thrives in a fast-paced environment. You're passionate about developing and delivering data analytics and machine learning models that drive business impact.

What We Offer:

• A collaborative and innovative work environment.
• Opportunities to work on cutting-edge AI/ML projects.
• A chance to make a significant impact on Ford Motor Company's business outcomes.
• Professional development and growth opportunities.
• A competitive salary and benefits package.
• If you're a motivated and collaborative Data Scientist with a passion for AI/ML, we encourage you to apply

Minimum Qualifications:

• Master's degree (M.S.) in Data Science, Computer Science, Business Analytics, Machine Learning, Statistics, or a related quantitative field.
• 3+ years of experience in AI/ML, with proven experience developing and deploying machine learning models in a production environment.
• Strong proficiency in Python for data science and machine learning.
• Expertise in SQL for data querying, manipulation, and database interaction.
• Strong skills in data acquisition, algorithm design, and model development and refinement.
• Experience with big data technologies, cloud-based data platforms (e.g., GCP, AWS), and business intelligence tools (e.g., Power BI, Streamlit, Plotly Dash).
• Solid understanding of machine learning algorithms, statistical modeling, and data analysis techniques.
• Excellent oral, written, and interpersonal communication skills.

Preferred Qualifications:

• Ph.D. in Data Science, Computer Science, Business Analytics, Machine Learning, Statistics, or a related quantitative field.
• 5+ years of experience in data science and analysis, including leadership or mentoring roles.
• Significant experience in Generative AI, including a strong understanding of ML frameworks, algorithms, and practical implementation.
• Experience in specialized areas such as Natural Language Processing (NLP), deep learning (e.g., TensorFlow, PyTorch), or recommendation systems.
• Experience with building and managing data pipelines, including familiarity with orchestration tools (e.g., Apache Airflow, Kubeflow) or DBT.
• Certifications in Google Cloud Platform (GCP) or other cloud platforms.
• Experience with agile development methodologies and version control systems (e.g., Git).
• A record of publications or presentations in recognized journals or conferences.

• Acquire a deep understanding of business problems and translate them into appropriate technical solutions.

• Design, develop, and implement end-to-end AI/ML pipelines, from data ingestion and preprocessing to model training, evaluation, and deployment.

• Act as a full-stack data scientist to develop and deliver advanced analytics models, including classification, time series, LLM, and more.

• Write clean, efficient, and well-documented code in Python for data manipulation, feature engineering, and model development.

• Utilize SQL extensively for data extraction, transformation, and loading (ETL) from various relational databases.

• Collaborate internally and with data engineers to identify new and novel data sources, ensure robust data infrastructure, and explore their potential use in developing actionable business results.

• Monitor and maintain deployed AI/ML models, ensuring their ongoing performance, accuracy, and reliability.

• Analyze and interpret complex datasets to identify trends, patterns, and insights that inform model development and business decisions.

• Communicate technical concepts and analytical results effectively to both technical and non-technical stakeholders.

• Work independently with minimal guidance, taking ownership of projects and delivering results.

• Foster a collaborative team environment, showing the highest respect for team members and their contributions.

• Stay updated with the latest advancements in AI, machine learning, and data science technologies.