What are the responsibilities and job description for the Data Scientist AI/ML - Dallas, TX position at TechniPros, LLC?
Job Details
Job Title: Data Scientist AI/ML
Location: Dallas, TX
Domain: Insurance
Long Term Contract
Looking for W2 Candidates. No C2C
Description:
- Strategic AI Leadership: Translate complex business challenges into clear, actionable AI/ML strategies and comprehensive technical roadmaps, ensuring alignment with organizational goals.
- Team Leadership & Mentorship: Guide, mentor, and develop a high-performing team of data scientists and machine learning engineers, fostering a collaborative culture of innovation, continuous learning, and technical excellence.
- End-to-End Solution Delivery: Oversee the entire machine learning lifecycle, from problem definition and data exploration to model design, training, validation, deployment, monitoring, and ongoing optimization.
Production Deployment: Lead the successful deployment of robust, scalable, and high-impact ML solutions into production environments, ensuring they generate measurable and significant business value. - Technical Excellence & Best Practices: Champion MLOps best practices, ensuring robust model governance, versioning, testing, and monitoring for all AI solutions.
- Technology Evaluation & Integration: Actively research, evaluate, and integrate new AI technologies, frameworks (including Agentic frameworks), tools, and cutting-edge research findings to maintain a competitive edge and drive innovation.
- Stakeholder Collaboration: Collaborate effectively with cross-functional teams, including engineering, product, and business units, to understand requirements, manage expectations, and ensure successful project delivery.
- Ethical AI Development: Apply a strong understanding of ethical AI principles, fairness, transparency, and data privacy throughout the design, development, and deployment of all AI solutions.
Technical Proficiency:
- Demonstrated proficiency in Agentic frameworks (e.g., Lang graph, CrewAI), Python, and SQL.
- Deep expertise in the end-to-end ML lifecycle, including model design, training,
- validation, deployment, and monitoring.
- Proficiency in major cloud platforms (e.g., AWS, Azure, Google Cloud Platform) for scalable AI solution development and deployment, including relevant services (e.g., SageMaker, Azure ML, Vertex AI).
- Experience with Machine Learning Frameworks such as TensorFlow, PyTorch, and Scikit-learn.
- Familiarity with data processing and manipulation libraries/tools like Pandas and Apache Spark.
- Understanding of MLOps tools and practices (e.g., MLflow, Kubeflow, Docker, Kubernetes) for model lifecycle management.
- Proficiency with version control systems, particularly Git.
- Experience with both SQL and NoSQL databases.
Best Regards:
Bindu M
Phone:
Email:
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