Job Title: Data Science Specialist Lead Engineer
Location: Scottsdale, Arizona
Experience: 12+ Years
Employment Type: Contract
Interview Type: In-Person or Webcam

The Data Science Specialist Lead Engineer will lead advanced analytics initiatives and provide technical leadership across data science, machine learning, and predictive modeling efforts. This role involves guiding project teams, designing scalable data solutions, and driving data-driven decision-making processes across the organization. The ideal candidate should have extensive experience in building and deploying production-grade models and working within cloud-based and enterprise environments.

• Lead end-to-end data science initiatives, from problem definition and requirement gathering to model deployment and performance management.

• Build advanced predictive models and machine learning algorithms to support business optimization and innovation.

• Develop scalable machine learning pipelines and automated analytics frameworks.

• Analyze complex datasets and generate actionable insights for cross-functional stakeholders.

• Oversee the development of data engineering workflows to ensure data availability, quality, and reliability.

• Collaborate with product, engineering, and business teams to align solutions with organizational goals.

• Provide mentorship and technical guidance to junior data scientists and analytics engineers.

• Perform statistical analysis, research emerging technologies, and evaluate new tools and frameworks.

• Document methodologies, model details, performance metrics, and results for internal and external reviews.

• Ensure compliance with data governance standards, privacy, and security protocols.

• Bachelor's or Master's degree in Data Science, Computer Science, Engineering, Mathematics, Statistics, or related discipline.

• 12+ years of professional experience in data science, machine learning, predictive analytics, or related fields.

• Strong hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-Learn, Keras, or MLlib.

• Proficiency in programming languages including Python and R; strong SQL expertise.

• Experience with big data technologies like Spark, Hadoop, Databricks, or Snowflake.

• Strong knowledge of cloud platforms such as AWS, Azure, or Google Cloud.

• Proven track record leading enterprise-level data science projects and production deployments.

• Experience with data visualization tools such as Tableau, Power BI, or Looker.

• Excellent analytical, problem-solving, and communication skills.

• Experience with MLOps tools such as MLflow, Kubeflow, Airflow, or SageMaker.

• Knowledge of NLP, deep learning, generative AI, or reinforcement learning methods.

• Experience working in Agile environments and DevOps culture.

• Familiarity with APIs, microservices, and containerized environments such as Docker or Kubernetes.

• Domain experience in finance, healthcare, retail, or manufacturing is an advantage.

• Strong leadership and stakeholder management capabilities.