Senior Data Scientist

A key Optimus client is currently seeking a Senior Data Scientist to join their newly created AI/ML team!

***Visa sponsorship is not offered. 4 days per week in the office in Houston, TX is required. THIS IS NOT REMOTE WORK***

In this role, you will lead a large-scale AI/ML project in its nascent stage: Design, develop, implement & maintain/improve initiatives across business units. You will turn innovative ideas into tangible solutions through predictive analytics, machine learning, optimization, and cluster analysis. As an essential team member of the IT Dept, you will set up the AI & ML agenda for the organization and its subsidiaries. You will work with the business to identify potential opportunities and create standards/best practice for the company's AI & ML function.

The incumbent must be able to:

operational needs into predictive analytics/reporting requirements to support data-driven solutions/decisions.
complex data insights in a clear and effective manner to stakeholders across the organization, which includes non-technical audience.
informed and stay current on all the latest data science techniques and technologies.
for exploring and implementing innovative solutions to improve data analysis, modeling capabilities, and business outcomes.
use case design and build teams by providing guidance/ feedback as they develop data science models and algorithms to solve operational challenges.
The incumbent must bring these skills/qualifications: Master's or PhD in Computer Science, Statistics, Applied Mathematics.
If degree is in non-related field, must have at least 5 - 7 years' experience in data science or a similar role.
Must be proficient in at least one analytical programming language relevant for data science, such as Python. R will be acceptable. Machine learning libraries & frameworks are a must. Must be familiar with data processing and visualization tools (e.g., SQL, Tableau, Power BI).
Must have experience with full Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, monitoring, and feedback loop.
Expertise in advanced analytical techniques (e.g., descriptive statistics, machine learning, optimization, pattern recognition, cluster analysis, etc.)
Experience in Supervised and Unsupervised Machine Learning including classification, forecasting, anomaly detection, pattern recognition using variety of techniques such as decision trees, regressions, ensemble methods and boosting algorithms.,
Experience with cloud computing environments (AWS, Azure, or GCP) and Data/ML platforms (Databricks, Spark).