Senior Manager - Data Science/ML/Ai

Senior Manager - Data Science/Ai/ML

Position Overview:
Our team is researching and developing the most advanced science and technology in the beverage industry to help drive business related innovation and growth. We continuously leverage cutting edge science and technology into our research program, including advanced data science approaches, to efficiently derive new insights from complex research data, leading to discovery of new knowledge, ingredients and beverage systems.

We are looking for a highly motivated data scientist to join our team - primarily focused on developing ML/Ai models able to predict taste properties for compounds and mixtures from biological data and chemical properties. The role will further provide data science support to various other initiatives within the flavor and ingredients research group. This will be achieved in close collaboration with scientists with diverse backgrounds and skillsets, other data scientists, and IT partners, as well as an extensive network of external research partners and experts.

Key Duties / Responsibilities:

1. Partner with research scientists on various projects to translate challenging research problems into impactful data science solutions.
2. Collaborate with research scientists, program owners, and business users to lead projects through the end-to-end data science lifecycle, including: data wrangling, exploratory analysis, hypothesis testing, modeling, rapid prototyping, business validation/testing, and operational deployment.
3. Build datasets from both historical data, publicly available data, consumer research data, and data collected by the research teams, against identified research problems.
4. Develop, train, test, and refine predictive models by applying advanced machine learning principles to diverse sets of large structured and unstructured data, to accurately predict sensory attributes (such as sweet taste properties) of unknown compounds and mixtures.
5. Document and report out new insights and findings both concisely and easy to understand.
6. Communicate complex concepts, workflows, and results to a variety of technical and non-technical stakeholders including executive management.
7. Maintain expertise and awareness of emerging data science techniques, technologies and potential business applications for ML/Ai.
8. Build and maintain a robust library of data science solutions, reusable templates, algorithms and supporting code.
9. Work with collaborators, CRO's and consultants to accelerate project work or access capabilities that are not available at TCCC and effectively manage the relationship and projects.
10. Manage in-silico / molecular modeling projects through external collaborators.

Educational & Professional Experience:

1. PhD (or Masters degree with commensurate years of experience) in the chemical or biological sciences, with a strong data science component - such as computational chemistry/biology, molecular modeling, complex data analysis and modeling.
2. 3+ years working as a data scientist developing, optimizing and deploying predictive models.
3. Demonstrated experience applying a range of statistical and modeling techniques including hypothesis testing, dimensionality reduction, supervised learning (classification and regression), graph neural networks, forecasting, and unsupervised clustering.
4. Experience gathering, interpreting and translating research and business requirements.
5. Proven strong leadership ability - lead own work and projects, effective decision making and making progress when faced with ambiguity and with no or minimal provided direction.
6. Demonstrated ability to communicate complex analytical concepts and results at multiple levels to both technical and non-technical audiences.

Skill/Knowledge Areas:

1. Advanced Knowledge of Machine Learning: Familiarity with machine learning algorithms and techniques, such as supervised and unsupervised learning, neural networks, and deep learning. Ability to apply machine learning models to chemistry-related problems, including drug discovery, molecular property prediction, and materials design.
2. Proficiency in Cheminformatics: Experience with cheminformatics tools and software. Skilled in handling chemical databases and managing chemical data.
3. Strong Foundation in Chemistry: Solid understanding of chemical concepts, structures, properties, and reactions. Ability to interpret chemical data and comprehend the implications of chemical interactions. Working knowledge of computational chemistry and in-silico modeling techniques and programs.
4. Programming Skills: Proficiency in programming languages commonly used in cheminformatics and machine learning, such as Python, R, and Java. Experience with data analysis libraries and machine learning frameworks, such as NumPy, SciPy, scikit-learn, TensorFlow, and PyTorch.
5. Data Handling and Manipulation: Skills in data preprocessing, cleaning, and transformation to ensure high-quality input for machine learning models. Ability to work with large datasets, including knowledge of database management and SQL.
6. Analytical Thinking and Problem-Solving: Strong analytical skills to interpret complex chemical and computational problems. Creative problem-solving abilities to develop novel solutions in cheminformatics.
7. Bioinformatics and Computational Biology Knowledge: Understanding of biological data and systems, especially for applications in drug discovery and development.
8. Research and Collaboration: Ability to conduct research, stay updated with current trends, and contribute to scientific literature. Excellent communication and teamwork skills to collaborate with chemists, biologists, data scientists, and other stakeholders.
9. Attention to Detail: Precision in handling chemical data and model development to ensure accuracy and reliability.
10. Continuous Learning: Eagerness to stay informed about advancements in cheminformatics, machine learning, and related fields. Willingness to learn new tools, methodologies, and technologies as the field evolves.
11. Ability to collaborate: with CRO's, university research groups, and consultants to understand and evaluate current needs and priorities and to accelerate project work.
12. Documentation and Communication: Demonstrate ability to provide a high level of traceable and consistent documentation of all simulation activities. Must be able to communicate objectives, effort loading, and results to a large mix of technical and non-technical audiences.
13. Strong aptitude for learning: and applying new technologies related to Data Science and Data Management as well as capability to understand molecular biology and computational chemistry concepts, familiar with terminology and strategy in order to translate research needs into data science solutions.

Skills: Leadership; Data Science; Graph Neural Network (GNN); Technical Project Management; Researching; Deep Neural Networks (DNNS); Data Analysis; Discovery; Molecular Biology; Computational Chemistry; Communication; Cheminformatics; Data Compilation; Teamwork; Computer Programming; Machine Learning Algorithms#J-18808-Ljbffr