2 days ago
Base Salary
$124k - $203k/yr
Responsibilities
- Design, build, optimize, and maintain scalable data pipelines and analytical datasets.
- Build ingestion, transformation, and feature pipelines across large-scale structured and unstructured datasets.
- Establish data quality, observability, reliability, performance, and governance practices.
- Analyze product, customer, telemetry, behavioral, feedback, support, and operational data to identify trends, anomalies, friction points, and opportunities.
- Develop features, metrics, cohorts, customer segments, statistical analyses, machine learning models, and predictive approaches.
- Apply regression, classification, clustering, anomaly detection, time-series analysis, forecasting, NLP, embeddings, LLMs, and Generative AI where appropriate.
- Evaluate models and hypotheses using appropriate statistical and machine learning techniques.
- Partner with Engineering and Product teams to convert ambiguous questions into measurable analytical problems and recommendations.
- Operationalize successful models against production datasets, including monitoring and retraining where appropriate.
- Measure the impact of resulting product and engineering changes on customer and product outcomes.
Requirements
- 10+ years of hands-on experience in Data Engineering building and supporting scalable data pipelines, transformations, and analytical datasets.
- Advanced proficiency in Python and SQL and experience with large-scale structured and unstructured datasets.
- Experience with Apache Spark, modern ETL/ELT frameworks, and cloud data platforms such as AWS, Azure, or GCP.
- Strong understanding of data modeling, data quality, transformations, performance optimization, and reliable analytical datasets.
- Working knowledge of statistics, probability, hypothesis testing, regression, classification, clustering, and predictive modeling.
- Hands-on exposure to pandas, NumPy, scikit-learn, XGBoost, TensorFlow, or PyTorch.
- Experience or exposure to behavioral analytics, anomaly detection, segmentation, forecasting, predictive analytics, or applied Data Science techniques is highly desirable.
- Familiarity with NLP, LLMs, embeddings, vector databases, RAG, and Generative AI is beneficial.
- Coursework, certification, specialization, or a degree in Data Science, Machine Learning, Statistics, Applied Mathematics, Computer Science, or a related discipline is preferred.
- Ability to translate loosely defined product or customer questions into measurable analytical problems and communicate findings to technical and non-technical stakeholders.
- Demonstrated ability and motivation to develop deeper Data Science and Machine Learning expertise while applying strong Data Engineering fundamentals.
Benefits
- Hybrid position based in Frisco, Texas, with onsite work required on an as-needed basis and work from a home office otherwise; candidates must be within commuting distance.
- No relocation assistance is offered.
- Bonus program.
- 401(k) retirement benefits.
- Medical, dental, vision, basic life, short-term disability, and long-term disability coverage.
- Paid parental leave, support and community involvement programs, and flexible work hours.
- 14 paid company holidays.
- Unlimited paid time off for exempt employees.
- 96 hours of sick time and 120 hours of vacation annually accrued for non-exempt employees.
Tech Stack
Categories
Data EngineeringML Engineering
About McAfee
McAfee builds consumer and small business cybersecurity software, including antivirus, identity and privacy protection, and VPN services that run across PCs and mobile devices. It sells primarily via subscriptions and OEM/ISP partnerships, and focuses on detection, threat intelligence, and allowlisting to reduce false positives. Founded in 1987 and headquartered in San Jose, it is a privately held company owned by private-equity firm Advent International.