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.