JOB DESCRIPTION:
Accountabilities:
- Work collaboratively with partners to develop clear, compelling requirements of analytic delivery and insights.
- Responsible for co-creating prototypes working with data scientists on validating proposed ideas and soliciting guidance from stakeholders, running test-driven development and continuous integration processes, support, and troubleshooting.
- Act as a domain expert in advanced analytics and bring best-in-class, innovative ideas to test and measure performance and impact on business requirements.
- Define creative solutions to business problems using advanced algorithms and models. Build and manage a high-performing team.
- Make business recommendations to senior leadership based on results/trends identified in data analysis.
- Responsible for setting and operationalizing the analytics strategy for the data office organization.
- Partner with the data engineering team, technology architects, and IT to build & deploy Machine Learning/AI pipelines that enable analytics reusable framework & algorithms.
- Define approaches and lead complex and multi-functional analytical projects using advanced computational algorithms.
- Lead initiatives involving exploration and analysis of data across platforms.
- Evolve iterative development processes to drive timely Data Science outcomes and reinforce the test-and-learn culture.
- Define standard methodologies, identify gaps, improve quality, and share advanced modeling techniques and takeaways.
- Use relevant knowledge of computer science fundamentals, distributed computing, and machine learning to help build scalable analytical solutions.
Essential Skills/Experience:
- Experience in Operations, manufacturing or related field.
- Bachelor's degree in Engineering, Computer Science, Mathematics, Statistics, with 7+ years experience in Operations data and 3+ years in data analytics, data structures, and data science.
- Storytelling capability of creativity, pragmatism, and out-of-the-box thinking.
- Proactive approach with hands-on/can-do attitude, with prototyping/modeling techniques.
- Experience with varied qualitative and quantitative research methods, sampling methodologies, and design thinking.
- Analytical thinking for assessing model performance, interpreting outcomes, and making data-driven decisions for model improvement.
- Demonstrated ability to provide actionable insights that move the business forward exponentially.
- Own all phases of the data science product lifecycle (exploratory data analysis, model development, model productionizing, rollout, and evaluation).
- Demonstrated knowledge of generative models (GANs, VAEs) and NLP.
- Solid understanding of machine learning algorithms and data pre-processing techniques.
- Excellent communication skills with experience presenting and communicating to leadership and partners.
- Fluency in analytical & computational techniques with advanced analytics software package deployment.