Enterprise Data Scientist

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Position: Enterprise Data Scientist (Greenville, South Carolina)

Duties: Leverage advanced analytics techniques to extract insights from vast and complex datasets, enabling data-driven decision-making across the organization. Collaborate closely with cross-functional teams to identify business problems, develop predictive models, and deploy scalable solutions that drive operational efficiency, enhance customer experiences, and unlock new opportunities for growth. Develop and implement a comprehensive AI strategy aligned with the organization’s business objectives, vision, and long-term goals. Identify opportunities for leveraging AI and machine learning technologies to solve business challenges, enhance processes, and drive innovation across different functional areas. Collaborate with key stakeholders, including executives, business leaders, and technical teams, to understand their needs, priorities, and concerns related to Al adoption. Collect, clean, and preprocess large datasets from various sources to ensure data quality and usability for analysis. Apply statistical techniques and machine learning algorithms to analyze data, identify patterns, and develop predictive models for solving business problems. Evaluate model performance, fine-tune algorithms, and iterate on models to improve accuracy and effectiveness over time. Deploy analytical solutions into production systems, integrate models with existing workflows, and ensure scalability and reliability of data-driven applications. Assess potential risks and challenges associated with Al implementation, including ethical considerations, data privacy issues, regulatory compliance, and cybersecurity threats, and develop mitigation strategies accordingly. Stay updated on the latest advancements in data science, explore new methodologies, and experiment with emerging technologies to drive innovation within the organization. Establish key performance indicators (KPIs) and metrics to measure the impact and ROI of Al projects, track progress, and continuously optimize strategies based on feedback and insights. Lead organizational change initiatives to foster a culture of Al adoption, build internal capabilities, and facilitate knowledge sharing and skill development among employees. Identify and engage with external partners, vendors, and experts to leverage their expertise, resources, and technologies to accelerate Al adoption and achieve strategic objectives. Serve as a thought leader and advocate for Al within the organization and in the broader industry community through participation in conferences, speaking engagements, publications, and networking activities. Support, consult, and direct activities of data scientists and analyst in the area of AI and data solutions.

Requirements: Master’s degree in Computer Science, Artificial Intelligence, or a related field (willing to accept foreign education equivalent) plus five (5) years of experience as Enterprise Data Scientist, or related occupation with advanced analytics experience in the field of data science, applying scientific data analytics methods to automotive (OEM) industry datasets.

Specific skills/other requirements: Must also possess the following (quantitative experience requirements not applicable to this section): lead cross-functional teams in the delivery of Al/ML solutions, building Al/ML models using computer vision, and automotive data sources in an automotive (OEM) environment; building generative Al use cases in an automotive (OEM) environment; implement and industrialize machine learning algorithms (clustering, gradient boosting, anomaly detection, and artificial neural networks) and their real-world advantages/drawbacks; deep learning architectures (RNN, CNN, and LSTM) and frameworks (Tensor flow and PyTorch); generative models including autoencoders, variational autoencoders (VAEs), and GANs, as well as the ability to implement and train these models for specific use cases; implementation of end-to-end Al use cases using No-code Al: Data robot and H2O; and programming languages: Python, Java, and C++, as well as database technologies such as SQL, Oracle, SQL Server, or NoSQL databases to manipulate data and draw insights from large data sets.

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