Data Scientist - Gen AI

Skills Required:

LLM, SQL, Statistics, API, AI/ML, GenAI, Google Cloud Platform

Skills Preferred:

Ability to communicate and work with cross-functional teams and all levels of management , LLM, Big Query,, Statistics, API, Microsoft Excel, Problem Solving, AI/ML, GenAI, Any cloud - GCP - Biq Query, Data Flow, Dataproc, Data Fusion, TERRAFORM, Tekton,Cloud SQL, AIRFLOW, POSTGRES, Airflow PySpark, Python, API

Experience Required:

Senior Associate Exp: 3 to 5 years experience in relevant field

Experience Preferred:

• Experience working within the Automotive industry or with related data such as vehicle telematics, manufacturing quality, supply chain, or customer behavior in an automotive context. • Experience with GCP services such as GCP Big query, GCS, Cloud Run, Cloud Build, Cloud Source Repositories, Cloud Workflows • Proficiency with specific dashboarding and visualization tools such as Looker Studio, PowerBI, Qlik, or Tableau. • Experience with SQL for data querying and manipulation. • Familiarity with big data technologies (e.g., Spark, Hadoop). • Experience with MLOps practices and tools for deploying and managing models in production. • Advanced degree (PhD) in Statistics or a related quantitative field.

Additional Information :

GenAI Development & Customization: Design, train, and deploy advanced generative models (LLMs, GANs, VAEs, diffusion models). Leverage prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG) to customize models for specialized corporate use cases. End-to-End Lifecycle & Ethics: Lead data science projects from problem definition to deployment. Rigorously validate and monitor models in production, ensuring performance, safety, and ethical AI compliance throughout their lifecycle. Scalable GenAI MLOps: Establish automated, cloud-based MLOps pipelines tailored for GenAI, bridging the gap between rapid prototyping and robust production deployment. Advanced Data Analytics: Apply statistical methodologies and ML algorithms to analyze massive structured and unstructured datasets—especially quality, product development, and connected vehicle data—to extract key patterns and power generative models. Applied Innovation: Pioneer applications for synthetic data generation, process automation, content creation, and advanced human-computer interaction. Stakeholder Collaboration: Partner with cross-functional teams to translate business needs into technical specs. Create interactive dashboards and present complex technical findings clearly to diverse audiences. Cloud & Best Practices: Build robust, automated pipelines leveraging cloud infrastructure. Stay at the forefront of ML, statistics, and cloud advancements, advocating for best-practice adoption.