As the Data Scientist, you will play a pivotal role in driving data-driven decision-making and advancing our organization's AI and analytical capabilities. You will lead a team of data scientists, collaborate with cross-functional teams, and contribute to the development and implementation of AI and advanced analytics solutions. This position requires a strong combination of technical expertise, leadership skills, and business acumen.

Responsibilities:

Team Leadership:

Lead, mentor, and inspire a team of junior data scientists, fostering a collaborative and innovative work environment.
Provide technical guidance, set priorities, and ensure the team's alignment with organizational goals.
Conduct regular performance assessments and contribute to professional development plans.

Strategy and Planning:

Collaborate with stakeholders to understand business objectives and identify opportunities for leveraging data to achieve strategic goals.
Develop and execute a data science roadmap, ensuring alignment with overall business and technology strategies.
Stay abreast of industry trends, emerging technologies, and best practices in data science.

Advanced Analytics and Statistical Modeling:

Design, develop, and implement advanced machine learning models and statistical algorithms to extract insights and solve complex business problems.
Apply robust statistical process control (SPC), univariate and multivariate analysis, and both parametric and non-parametric statistical techniques.
Conduct hypothesis testing, PCA, Shapiro-Wilk test, Anderson-Darling test, Box-Cox transformation, and other statistical methods to ensure data quality and model validity.
Work extensively with batch genealogy data and large manufacturing datasets to uncover patterns and optimize operational efficiency.
Ensure strong statistical analysis support for both normal and non-normal distributions

R Shiny Application Development:

Develop and maintain robust, interactive R Shiny applications to support dynamic data exploration and decision-making platforms.
Build scalable and user-driven front-end interfaces for real-time statistical analysis and visualization.
Collaborate with backend engineers to integrate R Shiny platforms with Redshift and other data sources for seamless analytics delivery.

Cross-functional Collaboration:

Collaborate with cross-functional teams, including business analysts, software engineers, and domain experts, to integrate data science solutions into business processes.
Communicate complex analytical findings to non-technical stakeholders in a clear and actionable manner.

Data Governance and Quality:

Establish and enforce data governance standards to ensure the accuracy, reliability, and security of data used for analysis.
Work with data engineering teams to enhance data quality and integrity throughout the data lifecycle.

Project Management:

Oversee the end-to-end execution of data science projects, ensuring timelines, budgets, and deliverables are met.
Provide regular project updates to stakeholders and manage expectations effectively.

Technical Expertise:

Provide technical guidance and execution for the latest GenAI technologies, including but not limited to LLM/SLM/VLM and Multi-modal AI Algorithms. Leverage Transformers for complex natural language processing-based tasks.
Lead the development of deep learning technologies like computer vision for image processing, OCR/IDP, object detection and tracking, segmentation, Image generation, Convolutional Neural Networks, Capsule Networks, etc
Development of core Machine Learning algorithms like time series analysis with Neural ODEs; Variational Autoencoders for Image Generation and anomaly detection;
Provide oversight for core deep learning algorithms like Neural Architecture Search for optimization and Graph Neural Networks for molecular structures.

Qualifications:

● Master's or Ph.D. in a quantitative field (Computer Science, Statistics, Mathematics, etc.)

● Minimum 1.5+ years of experience leading a team of junior data scientists, with a proven track record of successful project implementations.

● Proven experience in developing and deploying R Shiny applications for real-time analytics and statistical platforms.

●In-depth experience with SPC, hypothesis testing, PCA, Shapiro-Wilk test, Anderson-Darling test, Box-Cox transformation, and batch genealogy analysis.

● Experience in developing statistical solutions for both normal and non-normal distributions, and applying both univariate and multivariate techniques.

● Experience with GenAI, Agentic AI, LLM Training, and LLM-driven workflow development. Knowledge of large multi-modal models is a must.

● Experience in MLOps, Statistical Modeling, and Data Visualization.

● Must have experience with the development and implementation of various core Machine Learning algorithms mentioned above.

● Must have hands-on experience with Deep Learning technologies for computer vision and image processing, as well as core neural network applications like optimization.

● Experience in developing ML, AI, and Data Science solutions and putting solutions in production, with proficiency in Data Engineering, is desirable.

● Experience in the development and implementation of scalable and efficient data pipelines using AWS services such as SageMaker, S3, Glue, and/or Redshift.

● Excellent leadership, communication, and interpersonal skills.

● Experience with big data technologies and cloud platforms is a plus.