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R Hello, Urgent job openings for Data Science - Director @ GlobalData(Hyderebad) Job Description given below please go through to understand the requirement. if requirement is matching to your profile & interested to apply please share your updated resume @ mail id (m.salim@globaldata.com). Mention Subject Line :- Applying for Data Science - Director @ GlobalData(Hyd) Share your details in the mail :- Your Full Name : Mobile # : Qualification : Company Name : Designation : Total Work Experience Years : Work Experience in Data Science Years : Python Experience Years : Gen AI/LLM Experience Years : Team/Project size handled : Current CTC : Expected CTC : Notice Period : Current Location/willing to relocate to Hyd? : Office Address : 3rd Floor, Jyoti Pinnacle Building, Opp to Prestige IVY League Appt, Kondapur Road, Hyderabad, Telangana-500081. Job Description :- Role : Data Science and LLM Director Overview This combined role leads the organization's data science initiatives with a special focus on Large Language Models (LLMs), generative AI technologies, and intelligent automation. The position requires strategic leadership in data-driven decision making, specialized expertise in implementing cutting-edge AI language models, and proficiency in developing automated solutions to transform business operations and enhance organizational efficiency. Core Responsibilities :- Leadership & Strategy Lead and mentor a team of data scientists, ML and Python engineers, and automation specialists, fostering a culture of innovation, collaboration, and continuous learning Develop and execute the organization's data science, generative AI, and automation vision, strategy, and roadmap aligned with business objectives Identify and prioritize high-impact opportunities where data science, LLMs, and automation can drive business value and operational efficiency Partner with senior executives to ensure AI and automation initiatives support broader organizational goals Establish governance frameworks, ethical guidelines, and best practices for responsible AI deployment and automated process implementation Drive automation strategy to streamline workflows, reduce manual processes, and enhance productivity across data science operations Technical Direction Design scalable data processing pipelines and architecture for analytics, modeling, LLM implementation, and automated workflows Oversee the evaluation, fine-tuning, and deployment of large language models for specific business applications Drive implementation of data science, LLM, and automation solutions across various business functions including customer service, content generation, knowledge management, and process optimization Develop and implement intelligent automation strategies to streamline data workflows, model deployment, and operational processes Balance innovation with practicality, selecting appropriate technologies and automation tools based on business needs and ROI Guide the integration of LLMs with existing data infrastructure, systems, and automated processes Establish automated monitoring using Python , testing, and deployment pipelines for AI/ML models and data science solutions Applied Data Science Lead the development of predictive models, machine learning solutions, and LLM applications Ensure data quality and appropriate data management practices across initiatives Direct the collection of new data and refinement of existing data sources to improve model performance Oversee experimentation and validation processes to evaluate solution effectiveness Facilitate the translation of complex analytical findings into actionable business insights Communication & Education Present findings and recommendations to key stakeholders through reports and presentations Serve as the organization's point person for generative AI knowledge and applications Develop educational resources and training programs to build data science and AI literacy Communicate complex technical concepts to non-technical stakeholders in clear, understandable terms Prepare white papers, case studies, and other materials showcasing data science and LLM successes Qualifications, Education & Experience Master's or B,tech in Computer Science, Statistics, Mathematics, Data Science, Machine Learning or related quantitative field 10+ years of experience in data science, with at least 3+ years focusing on natural language processing or LLMs 7+ years of experience managing technical teams and leading complex projects 5+ years of hands-on experience in automation, process optimization, and workflow automation tools 8+ years of advanced Python programming experience, including data science libraries, automation frameworks, and ML/AI development Demonstrated success implementing data science and automation solutions that deliver measurable business impact Experience with large language model deployment, fine-tuning, and evaluation Proven track record in developing and implementing automated data pipelines, ML model deployment, and intelligent process automation using Python Technical Skills Expert-level programming skills in Python and proficiency with data science/ML libraries Strong background in machine learning, deep learning, and statistical analysis Hands-on experience with LLM frameworks such as PyTorch, TensorFlow, or Hugging Face Experience with MLOps and the automation of model training, evaluation, and deployment Proficiency with cloud platforms (AWS, GCP, Azure) and big data technologies Knowledge of data visualization tools and techniques for effective communication Leadership & Soft Skills Strategic thinking and business acumen with ability to align technical solutions to business needs Exceptional communication skills for conveying technical concepts to diverse audiences Experimental mindset with ability to balance innovation with practical implementation Strong project management capabilities and experience with agile methodologies Collaborative approach to working with cross-functional teams and stakeholders Key Performance Indicators Development and execution of data science and LLM strategy with measurable business impact Successful implementation of LLM-powered solutions that improve operational efficiency Quality and effectiveness of data science team output and deliverables Adoption of data-driven decision making across the organization Creation of scalable, sustainable data science and AI infrastructure This role reports directly to the Chief Technology Officer (or equivalent) and works closely with executives across Product, Engineering, Marketing, and Customer Success to drive data-powered innovation and business growth. Thanks & Regards, Salim (Human Resources)
