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SalaryJob Purpose
The Entry Level AI Data Scientist will support the development and implementation of artificial intelligence (AI) solutions within the organization while demonstrating a propensity for action over analysis. The incumbent will assist in designing, building, and maintaining AI models, algorithms, and data pipelines to extract insights and support decision-making processes. This role will contribute to driving innovation and leveraging AI technologies to improve business outcomes, while respecting the governance principles of responsible AI, data privacy, and ethical considerations.
Key Accountabilities
AI Model Development and Deployment:
- Assist in developing and deploying AI models and algorithms to solve business problems using machine learning techniques.
- Develop prototypes and production models using machine learning frameworks such as TensorFlow, Keras, PyTorch, etc.
- Collaborate with cross-functional teams to integrate AI models into existing systems and workflows, ensuring responsible AI practices are followed.
- Collect, clean, and preprocess structured and unstructured data for AI model training.
- Perform exploratory data analysis to identify patterns, trends, and anomalies while respecting data privacy regulations.
- Collaborate with cross-functional teams to define data requirements and ensure data quality, while adhering to data privacy and governance principles.
- Develop data visualizations to communicate findings to stakeholders.
- Work with stakeholders to define success criteria for models.
- Monitor and analyze the performance and drift of deployed AI models and algorithms, taking proactive actions to optimize their performance.
- Identify opportunities for model improvement, fine-tuning or retraining, and optimization, while ensuring ethical considerations are respected.
- Work closely with stakeholders to collect feedback and address issues or concerns, upholding responsible AI principles and ethics.
- Collaborate effectively with cross-functional teams to understand business requirements and align AI solutions accordingly.
- Document and maintain clear and organized records of data, models, and experiments.
- Share acquired knowledge and expertise within the team, while respecting governance guidelines.
- Document model architecture, parameters, and hyperparameters.
- Develop model metadata to enable tracking and versioning.
- Develop model performance metrics to monitor and track model effectiveness.
- Develop model monitoring and alerting processes to ensure models are performing as expected.
- Develop and maintain codebase for models using DevOps/MLOps tools.
- Work with stakeholders to ensure models are integrated into business processes and systems.
- Ensure compliance with ethical and legal standards related to data privacy, security, and model bias.
- Contribute to the development and implementation of ethical AI practices within the organization.
- Stay informed about regulations and industry guidelines impacting AI usage, while upholding responsible AI principles and governance standards.
Qualifications, Experience, and Skills
Required Work Experience:
- Solid understanding of AI concepts, machine learning algorithms, and statistical analysis.
- Proficiency in programming languages such as Python or R.
- Familiarity with data manipulation and analysis tools, such as SQL, Pandas, or NumPy.
- Excellent problem-solving and critical-thinking skills.
- Strong attention to detail and ability to work with complex data.
- Experience with AI frameworks and libraries, such as TensorFlow, PyTorch, Keras, or scikit-learn.
- Knowledge of big data technologies and distributed computing frameworks, such as Hadoop or Spark.
- Familiarity with natural language processing (NLP), computer vision, or other AI subfields.
- Familiarity with cloud-based machine learning platforms such as AWS SageMaker or Azure Machine Learning.
- Experience working with large-scale datasets and data pre-processing techniques.
- Proven communication skills: effective verbal, presentation, and written.
- Bachelor’s Degree in Technical discipline such as Computer Science, Physics, Mathematics, and Engineering or related field. Certifications and/or relevant experience are welcome in lieu of a deg.
- Model performance (accuracy and effectiveness) and feature selection.
- Quality and completeness of data pre-processing and analysis.
- Timely delivery of AI and machine learning models.
- Positive feedback from stakeholders regarding AI solutions and insights.
- Adherence to data governance and security policies.
Desired Skills:
- Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modelling, clustering, decision trees, neural networks, etc.
- Data science tools such as Jupyter Notebook. Open Data Hub (Seldon, Prometheus, Dataiku, IBM Watson Studio, etc).
- Experience in ML Ops (ex. GitHub) and Agile methodologies.
- Deep learning - machine learning that is a neural network with three or more layers, which helps to “learn” from large amounts of data.
- Cloud/big data tools (ex. blob storage, Redshift, Kafka, Hadoop, Spark, Hive etc.).
- Experience presenting data in BI Tools such as Tableau or PowerBI.
Working Conditions
- Tax Location (Work State): Ohio or California
- Overtime: As needed based on workload to achieve completion targets on time
- Travel: 5-10% required
Honda is an equal opportunity employer and considers qualified applicants for employment without regard to race, color, creed, religion, national origin, sex, sexual orientation, gender identity and expression, age, disability, veteran status, or any other protected factor.
