Senior Data Scientist

Job DescriptionSenior Data Scientist - Generative AIType: Direct Hire Location: Houston, TX - Onsite Position Overview An established technology-driven organization is seeking a highly experienced Senior Data Scientist to join a collaborative and fast-paced data and analytics team. This role is ideal for a hands-on technical leader with deep expertise in data science, machine learning, and data engineering, with a strong emphasis on designing, building, and deploying Generative AI solutions. The Senior Data Scientist will partner closely with engineering, product, and business stakeholders to deliver scalable AI/ML solutions that support data-driven decision-making and innovation across the enterprise. The environment emphasizes big data, advanced analytics, and modern ML platforms. Key ResponsibilitiesDesign, develop, and deploy advanced machine learning models, with a focus on Generative AI techniques (e.g., GANs, transformers, diffusion models, RAG-based systems). Build and train deep learning models using frameworks such as TensorFlow, PyTorch, or JAX for applications in NLP, computer vision, and other generative use cases. Collaborate with engineering teams to productionize AI/ML solutions, ensuring performance, scalability, and reliability. Partner with technical and business leaders to help shape data strategy, analytics vision, and supporting documentation. Apply advanced statistical methods and modeling techniques to analyze large, complex datasets and deliver actionable insights. Design and implement robust data pipelines for ingesting, cleaning, and transforming data for machine learning workflows, including lakehouse architectures and medallion-style data governance. Architect and maintain scalable data platforms and storage solutions supporting both batch and real-time processing. Own end-to-end ML workflows, including feature engineering, model training, evaluation, deployment, and monitoring. Continuously evaluate and optimize model performance and contribute to improvements in the broader AI/ML ecosystem. Mentor and guide junior data scientists and engineers, promoting best practices and technical growth. Stay current with emerging AI/ML research and apply innovative approaches to real-world business challenges. Clearly document and communicate technical findings and recommendations to both technical and non-technical audiences. Participate in, and at times lead, data and AI technology selection initiatives. Required QualificationsMaster's or PhD in Computer Science, Data Science, Engineering, or a related discipline with a concentration in machine learning, AI, or data engineering. Demonstrated hands-on experience with Generative AI, including GANs, VAEs, transformers, RAG pipelines, and other modern deep learning architectures. Strong programming skills in Python, R, or Julia, with extensive use of AI/ML libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Significant experience in data engineering, including ETL pipelines, data wrangling, real-time processing, and data storage solutions. Deep understanding of machine learning algorithms, deep learning methods, and statistical modeling. Experience working in cloud environments (AWS, Azure, or GCP) and with distributed computing frameworks such as Spark or Hadoop. Proven ability to design and optimize data architectures for both batch and streaming data use cases. Proficiency with SQL and NoSQL databases and data query languages. Strong analytical, problem-solving, and critical-thinking skills, with the ability to work autonomously or collaboratively. Excellent written and verbal communication skills, with the ability to translate complex technical concepts for diverse audiences. Preferred ExperienceHands-on experience with NLP and computer vision use cases, including large-scale pre-trained models (e.g., GPT, BERT, DALL• E). Experience deploying and monitoring ML models in production using tools such as Docker, Kubernetes, MLflow, or similar platforms. Familiarity with software engineering best practices, including version control (Git) and collaborative development workflows. Exposure to DevOps and CI/CD pipelines supporting machine learning systems. Published research or contributions to recognized AI/ML conferences, journals, or open-source projects.

Created: 2026-03-04