Data Scientist with Generative AI Experience

Qualifications

• Visa Type :- All Visa applicable which are ready for W2
• Min of 6+ years of experience in data science, with at least 2 years focused on developing and deploying Generative AI models (e.g., GANs, transformers)
• – Proven track record of building and implementing machine learning models and generating insights from data in a business context
• – Strong proficiency in machine learning frameworks and libraries, such as TensorFlow, PyTorch, and Scikit-Learn
• – Expertise in Generative AI models (e.g., GANs, VAEs, and transformer-based models like GPT, BERT) with hands-on experience in training, fine-tuning, and deploying these models
• – Proficient in data wrangling and preprocessing with tools like Pandas, NumPy, and experience in managing both structured and unstructured data
• – Programming proficiency in Python and R, with experience in other languages (e.g., SQL) for data manipulation and retrieval
• – Familiarity with cloud platforms (AWS, GCP, Azure) and their services for AI/ML, including model deployment and monitoring (e.g., AWS SageMaker, Google AI Platform)
• Generative AI Skills
• – Hands-on experience with large language models (LLMs) and understanding of advanced NLP techniques, such as text generation, summarization, and sentiment analysis
• – Knowledge of GAN architectures for synthetic data generation, image processing, and computer vision applications
• – Experience with fine-tuning pretrained models and applying transfer learning techniques to improve generative model performance
• – Understanding of evaluation metrics and methods for Generative AI models, ensuring models perform reliably and ethically in production
• Data Science and Analytical Skills
• – Expertise in statistical analysis, hypothesis testing, and interpreting data to derive actionable insights
• – Strong data visualization skills using tools like Matplotlib, Seaborn, or Tableau to communicate findings and model results to stakeholders
• – Experience in A/B testing, experimentation, and analysis to validate model performance and impact
• – Ability to define, measure, and monitor key performance indicators (KPIs) to assess model effectiveness and alignment with business goals
• – Proven ability to work with big data frameworks like Apache Spark or Hadoop for large-scale data processing
• Communication and Collaboration Skills
• – Strong communication skills, with the ability to explain complex AI concepts to non-technical stakeholders and collaborate across teams
• – Experience in documenting data science processes, model architectures, and analytical findings in a clear and organized manner
• – Proven ability to work collaboratively in cross-functional teams with engineers, product managers, and designers to align on project goals
• – Strong presentation skills for delivering data-driven insights and Generative AI use cases to business leaders and decision-makers
• – Familiarity with data privacy and ethical considerations in AI, including bias mitigation, fairness, and compliance with industry regulations
• – Demonstrated commitment to continuous learning, staying updated on the latest AI advancements and generative models
• – Knowledge of MLOps and model monitoring tools (e.g., MLflow, Kubeflow) to ensure model reliability in production
• Bachelor’s or Master’s degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field

Responsibilities

• Technical Skills
• – Familiarity with prompt engineering and model customization for specific applications (e.g., chatbots, content generation)

Full Description

Job Title :- Data Scientist with Generative AI Experience

Employment Type :- W2

Duration :- Long Term

Visa Type :- All Visa applicable which are ready for W2

Location- Atlanta, GA (Day-1 Onsite)

Job Description:

• Min of 6+ years of experience in data science, with at least 2 years focused on developing and deploying Generative AI models (e.g., GANs, transformers).

– Proven track record of building and implementing machine learning models and generating insights from data in a business context.

• Technical Skills

– Strong proficiency in machine learning frameworks and libraries, such as TensorFlow, PyTorch, and Scikit-Learn.

– Expertise in Generative AI models (e.g., GANs, VAEs, and transformer-based models like GPT, BERT) with hands-on experience in training, fine-tuning, and deploying these models.

– Proficient in data wrangling and preprocessing with tools like Pandas, NumPy, and experience in managing both structured and unstructured data.

– Programming proficiency in Python and R, with experience in other languages (e.g., SQL) for data manipulation and retrieval.

– Familiarity with cloud platforms (AWS, GCP, Azure) and their services for AI/ML, including model deployment and monitoring (e.g., AWS SageMaker, Google AI Platform).

• Generative AI Skills

– Hands-on experience with large language models (LLMs) and understanding of advanced NLP techniques, such as text generation, summarization, and sentiment analysis.

– Knowledge of GAN architectures for synthetic data generation, image processing, and computer vision applications.

– Experience with fine-tuning pretrained models and applying transfer learning techniques to improve generative model performance.

– Familiarity with prompt engineering and model customization for specific applications (e.g., chatbots, content generation).

– Understanding of evaluation metrics and methods for Generative AI models, ensuring models perform reliably and ethically in production.

• Data Science and Analytical Skills

– Expertise in statistical analysis, hypothesis testing, and interpreting data to derive actionable insights.

– Strong data visualization skills using tools like Matplotlib, Seaborn, or Tableau to communicate findings and model results to stakeholders.

– Experience in A/B testing, experimentation, and analysis to validate model performance and impact.

– Ability to define, measure, and monitor key performance indicators (KPIs) to assess model effectiveness and alignment with business goals.

– Proven ability to work with big data frameworks like Apache Spark or Hadoop for large-scale data processing.

• Communication and Collaboration Skills

– Strong communication skills, with the ability to explain complex AI concepts to non-technical stakeholders and collaborate across teams.

– Experience in documenting data science processes, model architectures, and analytical findings in a clear and organized manner.

– Proven ability to work collaboratively in cross-functional teams with engineers, product managers, and designers to align on project goals.

– Strong presentation skills for delivering data-driven insights and Generative AI use cases to business leaders and decision-makers.

• Additional Qualifications

– Familiarity with data privacy and ethical considerations in AI, including bias mitigation, fairness, and compliance with industry regulations.

– Experience in a specific industry like Telecommunications is a plus.

– Demonstrated commitment to continuous learning, staying updated on the latest AI advancements and generative models.

– Knowledge of MLOps and model monitoring tools (e.g., MLflow, Kubeflow) to ensure model reliability in production.

Education:

• Bachelor’s or Master’s degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field.

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