Principal Data Scientist

Job Description

Develop and Deploy predictive models in production and conduct advanced analytics, data mining, and data visualization to influence strategic decisions Architect and build data models to transform data into insights at scale Evaluate model performance and conduct iterative model training to maximize predictive and forecast accuracy on an on-going basis. Stay up to date with the latest advancements in AI/ML and GenAI, and drive innovation by evaluating new methodologies, architectures, and tools. Optimize data infrastructure and implement MLOps best practices to automate training, monitoring, and deployment of ML models. Collaborate with engineering teams to integrate AI models into production systems, ensuring scalability, security, and operational efficiency. Requirements: Bachelor or above degree in Computer Science, Applied Mathematics, Statistics, Econometrics, or related field 10+ years of industry experience in data science, machine learning, advanced analytics, with a proven track record of leading impactful projects. Exceptional analytical and problem-solving skills, with the ability to break down complex business challenges into data-driven solutions. Deep expertise in statistical modeling, machine learning, and predictive analytics, with hands-on experience deploying models at scale. Strong programming skills in Python (or similar languages) and extensive experience with libraries such as pandas, numpy, scipy, and scikit-learn Experience with big data frameworks like Spark and Databricks, including optimization for large-scale data processing. Proficiency in working with large, high-dimensional datasets, integrating multiple data sources, and deriving meaningful insights. Expertise in Generative AI (GenAI), including LLMs, transformer architectures (GPT, BERT, T5, etc.), and diffusion models (Good to have) Experience with cloud platforms (AWS, Azure, or GCP) and working knowledge of ML model deployment (MLOps) and AI/ML lifecycle management Proficiency in databases, including SQL (MySQL, SQL Server, PostgreSQL), NoSQL (MongoDB, Cassandra), or data warehouses (Snowflake, BigQuery, Redshift). Strong leadership skills, with experience mentoring junior data scientists and collaborating cross-functionally with product, engineering, and business teams. Ability to communicate technical concepts to non-technical stakeholders and drive data-informed decision-making across the organization