Outcome-Driven Accountability
- Collaborate with business and IT leaders to develop and refine ideas for integrating predictive and prescriptive analytics within business processes, ensuring measurable customer and business outcomes.
- Decompose complex business problems into manageable components, facilitating the use of multiple analytic modeling methods for holistic and valuable solutions.
- Develop and refine prototypes and proofs of concepts, presenting results to business and IT leaders, and demonstrating the impact on customer needs and business outcomes.
Technical Leadership and Advocacy
- Engage in data analysis, generating and testing hypotheses, preparing and analyzing historical data, identifying patterns, and applying statistical methods to formulate solutions that deliver high-quality outcomes.
- Develop project plans, including resource needs and task dependencies, to meet project deliverables with a focus on incremental and iterative delivery.
Engineering Craftsmanship
- Participate in defining project scope, objectives, and quality controls for new projects, ensuring alignment with customer-centric engineering principles.
- Present and communicate project deliverable results, emphasizing the value delivered to customers and the business.
Customer-Centric Engineering
- Assist in recruiting and mentoring team members, fostering a culture of engineering craftsmanship and continuous learning.
Incremental and Iterative Delivery
- Stay abreast of changes in technology, leading new technology evaluations for predictive and statistical analytics, and advocating for innovative, lean, and feasible solutions.
Key Responsibilities
- Create plans as proof-of-concept and presenting related results including prototypes to business and IT leaders.
- Develop project plans that include specification of resource needs and task dependencies to meet project deliverables.
- Direct data preparation planning effort and ETL procedure development and testing.
- Design and interpreting analytical models to discover data-driven insights that are actionable and transformative for restructuring strategies and operations to drive business value.
- Evaluate the performance of LLM models. This may involve measuring the models' accuracy on our tuned datasets.
- Design and deploy predictive and prescriptive models that may include LLM models to support business applications. Ability to package the model, create REST APIs, and deploy on cloud environments.
- Contribute to thought leadership initiatives, hackathons, internal innovation initiatives to promote PxE's reputation in predictive and generative analytics.
- Stay updated with technological advancements and participate in evaluations of new technologies for predictive, statistical, and generative analytics.
- Utilize Databricks for scalable data engineering, collaborative data science, and machine learning workflows.
- Leverage Dataiku for data science and analytics platform capabilities, including data preparation, visualization, and model deployment.
- Integrate Generative AI technologies to create innovative data-driven solutions and enhance analytics capabilities.
Education:
- Advanced degree in quantitative discipline (Statistics, Math, Computer Science, Engineering) or relevant experience.
Qualifications:
- Proven track record of leveraging data analytic methods and experience with very large datasets to create data-driven insights thru predictive and prescriptive analytic models.
- Extensive knowledge of supervised and unsupervised analytic modeling techniques such as linear and logistic regression, support vector machines, decision trees / random forests, Naïve-Bayesian, neural networks, association rules, text mining, and k-nearest neighbors among other clustering models.
- 7+ years of experience with data analysis software for large scale analysis of structured and unstructured data.
- 7+ years of experience with data mining, statistical modeling tools and underlying algorithms.
- Experience leading project teams.
- Ability to communicate complex quantitative analysis in concise and actionable manner.
- Proven ability to work independently and manage multiple task assignments.
- Strong oral and written communication skills, including presentation skills.
- Strong problem solving and troubleshooting skills with the ability to exercise good judgment.
- Strong time management and multi-tasking skills that have been demonstrated by track-record of working on multiple projects and cross-functional teams with staggered deadlines.
- Aptitude with Exploratory Data Analysis of large, complex datasets to arrive at useful insights and hypotheses.
- Working knowledge of such supervised and unsupervised learning techniques as Regression/Generalized Linear Models, decision tree analysis, boosting and bagging, Principal Components Analysis and clustering methods.
- Experience with the following technologies/methodologies required:
- Experience with the following technologies/methodologies preferred:
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Disclaimer: Please note that this description is subject to change basis business/engagement requirements and at the discretion of the management.
