Data Scientist / Big Data Engineer

Cayuse Holdings•Cedar Park, TX

10d•Hybrid

About The Position

The Data Scientist (Big Data Engineer) makes value out of data. They will proactively fetch information from various sources and analyze it for a better understanding about how the business performs. They will then use that information to build AI tools that automate certain processes within the company. This position performs all duties and responsibilities in accordance with the Mission, Vision, and Core Values of Cayuse.

Requirements

Demonstrated expertise in constructing distributed machine learning pipeline from scratch
Demonstrated experience performing end-to-end project delivery, including data visualization, performing research, and conducting presentations for non-technical audiences.
Strong documentation, organizational, and time management skills.
Strong verbal and written communication skills to effectively collaborate with team members and stakeholders.
Must be able to pass a background check. May require additional background checks as required by projects and/or clients at any time during employment.
Experience with machine learning frameworks and techniques, including regression, classification, neural networks, and feature engineering.
Experience with Azure ML, Power BI, or similar analytics platforms.
Skilled in web scraping, handling large-scale multi-source datasets, and managing ambiguous data environments.
Strong knowledge of statistical methods for experimentation (e.g., randomization, t-tests, and confidence intervals).
Proven track record of managing and delivering high-volume analytics or experimentation projects across multiple stakeholders.
Ability to translate analytical insights into actionable business recommendations.
Strong analytical and problem-solving skills with keen attention to detail.

Nice To Haves

Bachelor’s or master’s degree in Computer Science, Information Technology, Data Science or related field.
Proficiency in Python (pandas, numpy, scipy) and SQL for data manipulation, analysis, and pipeline development.
Knowledge of data science toolkits such as NumPy, Pandas and MatLab.
Experience with Azure and Databricks platforms.

Responsibilities

Perform data mining, exploration, and analysis.
Design, train, evaluate, and implement machine learning algorithms and predictive models.
Work with stakeholders to identify opportunities for improving business processes and addressing current pain points, using data insights, forecasts, and analytics.
Develop and implement analytics-enabled solutions to improve business processes, generate insights, and support business goals.
Develop Key Performance Indicators (KPIs), metrics, trend tracking, prediction, and forecasting models.
Evaluate and understand complex issues from business partners and recommend solutions.
Work closely with team throughout all phases of projects: requirements definition, design, development, test and evaluation, demonstration, and deployment.
Build, implement, and maintain predictive models throughout their full lifecycle.
Identify patterns and trends in data and provide insights to enhance business decision making.
Set up and monitor monthly model recalibration process.
Generate complex ad-hoc analyses combining disparate concepts or creating new approaches.
Design and create new analytic procedures and automation.
Other duties as assigned.