Scientist (Ai Modeling for Computational Sciences)

**Join us and contribute to the discovery of medicines that will impact lives**:
**Hyderabad, India | Hybrid | Full-Time**:
**About Aganitha**:
High-throughput experimentation, computational & data-driven modeling, and the advent of the Open Science era are fundamentally transforming research, discovery, and development across diverse industries. Aganitha is at the forefront of co-innovation with global clients, shaping next-generation R&D (aganitha.ai). While our primary client base has been in global Biopharma, we are actively expanding our collaborations into consumer brands and, in the near future, the materials design industry.

You will collaborate closely with research leaders at our client organizations, identifying their needs and designing innovative solutions. Working with our internal technical and scientific teams, you will drive solutions from concept to launch and growth. You may also interact with external vendors to coordinate experimental validation of the _in silico_ solutions developed at Aganitha.

**Key Responsibilities**:

- ** Design and develop AI/ML models** to solve complex problems, such as predicting molecular properties, material behaviors, or reaction outcomes.
- ** Curate, process, and analyze scientific datasets** from various sources, including literature and experimental data, ensuring data quality and readiness for model training.
- ** Develop intelligent featurization strategies** that accurately represent chemical structures, physical properties, or biological interactions, drawing upon your scientific understanding.
- ** Implement, train, and evaluate cutting-edge Machine Learning and Deep Learning algorithms** (e.g., CNNs, RNNs, LSTMs, Transformer architectures) to build robust predictive models.
- ** Rigorously validate models** against client-provided data and established benchmarks, ensuring their accuracy, generalizability, and utility.
- ** Translate complex technical and scientific findings** into clear, actionable insights for both technical and non-technical stakeholders.
- ** Collaborate effectively** with computational chemists, data scientists, and wet lab scientists to define project requirements, iterate on solutions, and ensure successful deployment.

**Qualifications**:

- ** PhD, post-doctoral research, or equivalent higher studies** in Computational Chemistry, Cheminformatics, Materials Science, Chemical Engineering, Biochemistry, Computational Biology, Bioinformatics, Computational Physics, Medicinal Chemistry, Applied Mathematics, Applied Statistics, Fluid Dynamics or a closely related scientific discipline.
- Familiarity with one or more of the following core domain skills: High-throughput docking and QSAR studies, Atomistic or Coarse-grained Molecular Dynamics (including Enhanced MD using PLUMED, Langevin Dynamics, Brownian Dynamics, and Dissipative Particle Dynamics), Periodic or non-periodic Quantum Chemical Calculations, Ab initio MD simulations and QM/MM simulations, Cheminformatics using RDKit, Mathematical Modeling, or Computational Fluid Dynamics.
- ** Strong foundational understanding** of the scientific principles underlying computational chemistry, materials science, or related fields (e.g., colloidal chemistry, polymer science, surfactant chemistry, soft condensed matter physics, etc.).
- ** Solid mathematical intuition** of Machine Learning algorithms, including Deep Learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, and Transformer architectures.
- ** Proficiency in modular, typed, and object-oriented Python programming.**:

- ** A high-level understanding of the ML/DL project lifecycle**, from data preparation and feature engineering through model development, training, evaluation, and deployment.
- ** Strong verbal and written communication skills**, with the ability to effectively communicate technical concepts to diverse audiences.

**Nice to Have**:

- Experience in effectively building and deploying ML solutions using popular frameworks such as PyTorch, TensorFlow, Keras, or scikit-learn.
- Proficiency in shell scripting.
- Proficiency in working with Linux-based operating systems (e.g., Ubuntu, Fedora, CentOS, Debian).

**Big Plus**:

- Exposure to large language models (LLMs) such as ChatGPT, Claude, or Gemini, and practical experience in utilizing such tools for day-to-day work (e.g., writing research reports, understanding new concepts, generating code).
- Experience in optimal usage of High Performance Computing (HPC) resources.
- Familiarity with container-based usage of software and their dependencies (e.g., Docker).