Note: The job is a remote job and is open to candidates in USA. Precision Technologies is seeking a highly accomplished Lead Data Scientist to lead the design, development, and deployment of enterprise-scale data science, machine learning, and AI-driven solutions. The ideal candidate will possess deep expertise in various data science methodologies and will be responsible for driving enterprise AI initiatives and optimizing model performance.
Responsibilities
- Lead enterprise-wide Data Science and Machine Learning initiatives from concept to production
- Design, develop, and deploy predictive models, machine learning algorithms, and advanced analytics solutions
- Build scalable AI and Generative AI applications using LLMs, RAG, and intelligent automation frameworks
- Perform exploratory data analysis (EDA), feature engineering, statistical modeling, and model evaluation
- Develop end-to-end ML pipelines including data ingestion, preprocessing, training, deployment, and monitoring
- Collaborate with Data Engineers, AI/ML Engineers, Architects, Product Owners, and business stakeholders
- Optimize model accuracy, scalability, reliability, and production performance
- Implement MLOps best practices for model versioning, deployment automation, governance, and monitoring
- Translate business problems into data-driven AI and analytics solutions
- Mentor Data Scientists and establish enterprise data science standards and best practices
Skills
- 12–15+ Years (Data Science, Machine Learning & Advanced Analytics Experience)
- H4 EAD, L2S, GC EAD, Green Card, US Citizen work authorization
- Deep expertise in Statistical Modeling, Machine Learning, Deep Learning, Predictive Analytics, Large Language Models (LLMs), and Cloud-Based AI Platforms
- Proven leadership experience delivering scalable data science solutions that drive business value
- Ownership of data science strategy, model development, AI innovation, predictive analytics, model deployment, MLOps, and enterprise AI initiatives
- Lead enterprise-wide Data Science and Machine Learning initiatives from concept to production
- Design, develop, and deploy predictive models, machine learning algorithms, and advanced analytics solutions
- Build scalable AI and Generative AI applications using LLMs, RAG, and intelligent automation frameworks
- Perform exploratory data analysis (EDA), feature engineering, statistical modeling, and model evaluation
- Develop end-to-end ML pipelines including data ingestion, preprocessing, training, deployment, and monitoring
- Collaborate with Data Engineers, AI/ML Engineers, Architects, Product Owners, and business stakeholders
- Optimize model accuracy, scalability, reliability, and production performance
- Implement MLOps best practices for model versioning, deployment automation, governance, and monitoring
- Translate business problems into data-driven AI and analytics solutions
- Mentor Data Scientists and establish enterprise data science standards and best practices
- Machine Learning
- Deep Learning
- Predictive Analytics
- Statistical Modeling
- Data Mining
- Feature Engineering
- Model Evaluation
- Time Series Forecasting
- Classification
- Regression
- Clustering
- Python
- R
- SQL
- NumPy
- Pandas
- SciPy
- Scikit-learn
- Large Language Models (LLMs)
- OpenAI GPT
- Azure OpenAI
- Claude
- Gemini
- Llama
- Prompt Engineering
- RAG (Retrieval-Augmented Generation)
- LangChain
- LlamaIndex
- TensorFlow
- PyTorch
- Keras
- Natural Language Processing (NLP)
- Computer Vision
- AWS
- Azure
- Google Cloud Platform (GCP)
- MLflow
- Kubeflow
- Docker
- Kubernetes
- CI/CD Pipelines
- Model Deployment
- Model Monitoring
- Databricks
- Apache Spark
- PySpark
- Snowflake
- SQL Server
- PostgreSQL
- Data Warehousing
- Power BI
- Tableau
- Matplotlib
- Seaborn
- Plotly
- Proven experience leading enterprise Data Science and AI teams
- Strong expertise in designing scalable AI and analytics solutions
- Experience delivering production-grade machine learning and Generative AI applications
- Ability to translate complex business requirements into data-driven solutions
- Strong stakeholder management, executive communication, and leadership skills
- Experience mentoring teams and driving enterprise AI adoption
- Experience building enterprise AI platforms and intelligent automation solutions
- Exposure to Agentic AI, Vector Databases, and RAG architectures
- Microsoft Azure AI Engineer, AWS Machine Learning, Google Professional ML Engineer, or Databricks certifications
- Experience in Banking, Healthcare, Insurance, Retail, Manufacturing, or Financial Services domains
- Strong understanding of Responsible AI, AI Governance, and Explainable AI (XAI)
Benefits
- Employment Type: Full-Time (W2 Only)
- Location: USA (Hybrid / Onsite)
- Work Authorization: H4 EAD, L2S, GC EAD, Green Card, US Citizen
Company Overview