FASTTEK GLOBALpowered by Fast Switch - Great Lakes
Chennai, Tamil Nadu
Data Science and Machine Learning Senior Associate #1058064Job Description:
Employees in this job function are responsible for predicting and/ or extracting meaningful trends/ patterns/ recommendations from raw data, leveraging data science methodologies including Machine Learning (ML), predictive modeling, math, statistics, advanced analytics, etc.
Key Responsibilities:
- Understand business requirements and analyze datasets to determine suitable approaches to meet analytic business needs and support data-driven decision-making
- Design and implement data analysis and ML models, hypotheses, algorithms and experiments to support data driven decision-making
- Apply various analytics techniques like data mining, predictive modeling, prescriptive modeling, math, statistics, advanced analytics, machine learning models and algorithms, etc.; to analyze data and uncover meaningful patterns, relationships, and trends
- Design efficient data loading, data augmentation and data analysis techniques to enhance the accuracy and robustness of data science and machine learning models, including scalable models suitable for automation
- Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy and robustness
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.
Education Required:
- Bachelor's Degree
Education Preferred:
- Master's Degree
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.