Develop and implement statistical forecasting models, utilize regression analysis, collect and transform large datasets, engineer features, develop ETL pipelines, and deploy machine learning models including deep learning techniques.
Responsibilities
Develop and implement statistical forecasting models to predict future market demand, sales trends, and customer behavior
Utilize regression analysis techniques to understand the relationships between various factors and business outcomes
Collect, clean, and transform large datasets from various sources, including internal databases, external APIs, and public data repositories
Engineer relevant features from raw data to improve model performance and extract meaningful insights
Develop and implement data pipelines for efficient data extraction, transformation, and loading (ETL)
Develop, implement, and optimise data models and algorithms to address business problems and improve decision-making.
Perform data mining, statistical analysis, and predictive modelling to identify trends, patterns, and actionable insights.
Design, develop, and deploy machine learning models using algorithms such as K-Nearest Neighbours, Naïve Bayes, Support Vector Machines, Decision Trees, and Random Forest.
Apply statistical and analytical methodologies, including hypothesis testing, experimental design, and data validation.
Use data science tools and programming languages, with hands‑on proficiency in Python or R.
Requirements
Bachelor's degree in Engineering, Computer Science, or a related discipline
The DDSI team sits at the intersection of business strategy, advanced analytics, and technology integration to help our partners explore opportunities for analytics, shape business decisions, and drive value.
Compensation
$80-$88/hr
Responsibilities
Achieve business goals and objectives—Research and develop decision science models and act as a consultant to all business units
Pursue innovation—Conduct research on analytical techniques and translate that research into usable and sustainable solutions for users
Put your skills to the test—Perform data collection and data mining, and build state-of-the-art decision science algorithms, tools, and systems
Drive value—Model and analyze revenue management and pricing related issues using various mathematical, statistical, and simulation techniques
Tell the story—Present science results to business partners and clients
Requirements
Master's or Ph.D. degree in Statistics, Operations Research, Industrial Engineering, Mathematics, Machine Learning, Econometrics, or related field
Lead a team of Data Scientists to translate business problems into applied statistical, machine learning, simulation, and optimization solutions that drive business value.
Locations (6)
San Antonio, Texas, United States
Plano, Texas, United States
Phoenix, Arizona, United States
Colorado Springs, Colorado, United States
Charlotte, North Carolina, United States
Master's degree in Data Science, Statistics, Computer Science, or a related field
Compensation range: The salary range for this position is: $189,370 - $361,950.
Responsibilities
Lead and develop team to build and deploy advanced analytical solutions
Manage project portfolio and communicate progress to senior leadership
Ensure compliance with model risk management policies
Research emerging technologies and drive adoption of advanced analytics
Simplify processes across model development lifecycle
Requirements
Bachelor's degree in Economics, Finance, Statistics, Mathematics, Actuarial Sciences, Operations Research, Data and/or Business Analysis, Data Science, or a business or quantitative field; OR 4 years of relevant education and/or experience
Advanced Degree (e.g., Master's, PhD) in Mathematics, Statistics, Data Science, Computer Science, or related quantitative STEM field and 6 years in predictive modeling, model governance, machine learning and large data analysis
