Industry expectations for Mid to Sr level Data Science professionals - 2 sample JDs and skills

-

 JD 1:

< This is from a well known company>

Here is the cleaned-up and streamlined job description for the Director – Data Science (LLM/Gen AI) role, with all recruiter info, marketing text, job board content, and unrelated company info removed. Only content relevant to understanding the role and responsibilities has been retained:

Role: Director – Data Science (LLM/Gen AI)

Experience Required: 15–20 years
Location: Hyderabad (Hybrid )
Employment Type: Full Time, Permanent

Role Summary:

This is a leadership role responsible for driving the organization’s data science, generative AI, and automation strategy. The role focuses heavily on Large Language Models (LLMs), intelligent automation, and applied machine learning, requiring strong strategic, technical, and team leadership skills.

Key Responsibilities:

Leadership & Strategy

  • Lead and mentor data scientists, ML engineers, and automation experts.

  • Define and execute the organization's AI, LLM, and automation vision and roadmap.

  • Identify high-impact opportunities for data-driven transformation.

  • Establish governance, ethics, and best practices for AI and automation.

  • Drive initiatives that improve operational efficiency and business outcomes.

Technical Direction

  • Design scalable data pipelines and architectures for analytics and ML.

  • Lead deployment and fine-tuning of LLMs for business use cases.

  • Oversee automation of data science workflows and model lifecycles.

  • Guide integration of LLMs into existing systems and infrastructure.

  • Implement MLOps strategies for continuous delivery and monitoring.

Applied Data Science

  • Direct development of ML models, predictive analytics, and GenAI applications.

  • Ensure data quality and proper data management practices.

  • Drive experimentation and validation of models.

  • Translate analytical findings into actionable business strategies.

Communication & Education

  • Present insights and strategy to executives and stakeholders.

  • Build internal capability through education and training on GenAI and LLMs.

  • Serve as the primary internal expert on LLMs and their applications.

Required Qualifications:

  • Education:

    • Master’s or B.Tech in Computer Science, Data Science, ML, Statistics, or related field.

  • Experience:

    • 10+ years in Data Science, including:

      • 3+ years in LLMs or NLP.

      • 8+ years in Python programming.

      • 5+ years in automation or intelligent workflows.

      • 7+ years managing technical teams and large projects.

Technical Skills:

  • Strong Python programming with libraries for ML and automation.

  • Experience with ML frameworks: PyTorch, TensorFlow, Hugging Face.

  • MLOps expertise: Model training, evaluation, deployment pipelines.

  • Cloud proficiency: AWS, Azure, or GCP.

  • Statistical analysis, deep learning, and NLP techniques.

  • Familiarity with big data tools and data visualization best practices.

Leadership & Soft Skills:

  • Strategic thinking with strong business acumen.

  • Clear communicator of technical ideas to non-technical audiences.

  • Experience in Agile environments and cross-functional collaboration.

  • Ability to balance innovation with business practicality.

Key Performance Indicators (KPIs):

  • Successful execution of data science and LLM strategy.

  • Deployment of high-impact GenAI and automation solutions.

  • Team performance and project delivery quality.

  • Increased adoption of AI-driven decision-making.

Let me know if you'd like a one-page version for internal reference, benchmarking, or recruitment comparison.

=========

JD2: 

Role: Manager – Data Science

Experience Required: 8–12 years
Location: Hyderabad
Employment Type: Full Time, Permanent

Role Summary:

This is a hands-on leadership role focused on developing, optimizing, and deploying AI/ML models. The manager will lead a team of data scientists and drive AI initiatives from concept to production. The position requires deep technical expertise combined with strong project and team management skills.

Key Responsibilities:

AI & ML Development:

  • Build, train, and deploy ML models (classification, regression, forecasting, deep learning, LLMs, generative AI).

  • Perform feature engineering, statistical analysis, and model optimization.

  • Use big data tools (Hadoop, PySpark, Hive) for efficient data processing.

  • Create AI solutions across domains like Retail, BFSI, Healthcare, and eCommerce.

  • Automate and optimize ML pipelines for scalability.

End-to-End ML Lifecycle Management:

  • Manage model development, deployment, and monitoring.

  • Collaborate with data engineering teams to productionize models.

  • Use cloud platforms (AWS, Azure, GCP, Databricks) for deployment.

  • Design A/B testing strategies for evaluating model performance.

Project & Stakeholder Management:

  • Lead multiple AI/ML initiatives ensuring timely and high-quality delivery.

  • Translate business problems into scalable AI solutions.

  • Communicate complex technical concepts clearly to non-technical stakeholders.

Team Leadership:

  • Mentor and guide a team of data scientists.

  • Set technical direction and enforce best practices in AI/ML.

  • Foster continuous learning and innovation within the team.

  • Support career development and technical growth of team members.

Required Skills & Qualifications:

  • 8+ years of hands-on experience in Data Science and AI.

  • Strong command of ML, Python, and SQL.

  • Practical exposure to Generative AI, including pilot or POC projects.

  • Deep understanding of NLP, LLMs, forecasting, and optimization techniques.

  • Proven experience in deploying models to production.

  • Proficiency in at least one cloud platform: AWS, Azure, GCP, or Databricks.

  • Solid foundation in statistics, probability, and causal inference.

  • Experience with big data tools (Hadoop, Hive, PySpark).

  • Bonus: Knowledge of digital analytics tools like Google Analytics or Adobe Analytics.

Education:

  • Undergraduate: Any Graduate

  • Postgraduate: Any Postgraduate


Comments

Post a Comment

Popular posts from this blog

Ranking of Airlines by Safety (Based on Accidents and Serious Snags, 2005–2025)

-
 Ranking of Airlines by Safety (Based on Accidents and Serious Snags, 2005–2025) Due to the scarcity of exact data on accidents and serious snags for each airline over 20 years, I’ll use safety rankings and incident records as a proxy, emphasizing fatal accidents, serious incidents, and reported snags (e.g., engine failures, system malfunctions, or near-misses). Airlines are ranked based on safety performance, with top-tier airlines having few or no fatal accidents and minimal serious snags. Top Safest Airlines (Few or No Fatal Accidents, Minimal Serious Snags) These airlines are consistently ranked among the safest, with no or few fatal accidents and limited serious snags from 2005 to 2025, per AirlineRatings.com’s 2025 safety rankings and other sources: Qatar Airways   Safety Record: No fatal accidents in its history (founded 1993). Tied for third in AirlineRatings.com’s 2025 safest airlines. Minor snags (e.g., technical delays), but no major safety-compromising inciden...
Read more

100 stable and 100 unstable job roles for 2025–2030

-
  Here’s a categorized version of the 100 stable and 100 unstable job roles for 2025–2030. I’ve grouped them into broader industry or functional categories to highlight patterns and provide clarity. Each category includes a brief note on its overall stability or instability, followed by the specific roles and their explanations. Stable Job Roles (2025–2030) These roles are grouped by industry or function, reflecting areas of growth, human necessity, or resistance to automation. Technology & Innovation (25 Roles) Category Note : Rapid advancements in AI, cloud computing, and robotics ensure demand for skilled tech professionals. AI/ML Engineer : Growing reliance on AI drives demand. Cybersecurity Specialist : Rising cyber threats make this critical. Data Scientist : Businesses need data-driven insights. Software Developer : Custom software remains essential. Cloud Computing Specialist : Cloud migration fuels demand. Robotics Engineer : Automation expansion needs designers. Block...
Read more

Thinking Patterns That Drive Success

-
  Thinking Patterns That Drive Success in Business and Professional Careers Effective thinking patterns and approaches significantly influence success in business and professional careers. Grounded in cognitive strategies and problem-solving methodologies, these thinking patterns shape decision-making, innovation, and resilience. Below are key thinking patterns, each illustrated with diverse examples across unrelated domains to demonstrate their universal relevance. 1. Growth Mindset Definition : Embracing challenges, persisting through setbacks, and viewing failures as learning opportunities. This fosters resilience and continuous skill development. Examples : Business : An entrepreneur learns from a failed product launch and pivots the offering based on customer feedback. Education : A student struggling with math embraces extra tutoring and improves over time. Sports : An athlete uses a series of losses to identify training gaps and eventually wins a championship. ...
Read more