Senior Data Scientist - Transforming Drug Discovery using AI| Pune

As a Senior Data Scientist at Innoplexus, you will play a key role in developing, designing, and implementing AI /DS algorithms that contribute to groundbreaking advancements in drug discovery. The challenges you will tackle involve mining vast datasets from hundreds and thousands of online literature & Real World Data to unveil novel relationships among diseases, treatments, and interventions. Your predictions will empower healthcare practitioners to estimate the success rates of patient treatments and clinical trials, driving transformative impacts in the field.

Your responsibilities will include, but are not limited to:

Develop, design, and implement advanced algorithms for extracting structured information from unstructured text data. Also, should be able to solve problem statements having low labeled information.

Extract conclusive information from extensive datasets to discover new relationships within medical, biological, and life sciences domains.

Make predictions to assist healthcare practitioners to boost the success rates of clinical trials.

Collaborate with cross-functional teams, including data scientists, software engineers, technology architects, and experts in GPU systems.

Conduct up-to-date and state-of-the-art advanced data analytics on acquired digital data, both using standard statistics and contemporary machine learning approaches.

Stay up on the state of the art in healthcare and life science to apply the latest advancements in the field to customer needs.

You will bring your curious, innovative, and collaborative mindset to make the best use of the absorption, distribution, metabolism, and excretion (ADME) data generated within our department and use state-of-the-art machine learning (ML) methods and statistical techniques to accelerate drug design.

Develop new algorithms, methods, and implement ML models to predict compound properties.

Apply ML data processing to understand relationships between structure and molecular properties.

 Investigate new algorithms or modeling strategies, identify research articles, and reproduce/apply innovative methodology to generate insights related to discovery data.

 Quickly learn and pilot the use of tools, data sources and analytical techniques needed to answer a wide range of critical business questions.

 In collaboration with cross-functional partners, provide scientific and strategic input to support discovery and lead optimization activities.

 Deliver clear and concise presentations for audiences with different expertise.

Must have Requirements:

Bachelor's degree in a highly analytical and quantitative field such as Computer Science, Electronics, Electrical, Theoretical Physics, Mathematics, etc. Master's degree or PhD preferred, preferably with a multidisciplinary background (cheminformatics, bioinformatics, biomedical engineering, AI/ML in drug discovery or life sciences).

4 to 7 years of relevant experience (5 to 8 years if holding a bachelor's degree).

4+ years of work-related experience in software development, deep learning, machine learning, NLP/LLM or high-performance computing.

Competency in at least one programming language (Python and C++ preferred; Haskell, Scala, or similar).

Experience with software development & modern Deep Learning software architecture and frameworks like Scikit-learn, Keras, Pytorch, TensorFlow and/or CUDA.

Experience with reproducible data science tools and practices. Previous research experience either in academia or industry.

Must enjoy interacting with forward-thinking people, life-long learning, and staying at the cutting-edge of the domain.

Excellent written & verbal communication and interpersonal skills in the presentation of highly technical material for effective collaboration in a multicultural environment and ability to translate analytical concepts for diverse audiences and stakeholders (English is our primary language).

Familiarity with collaboration tools and environments (e.g. OKRs, Jupyter notebooks, git, Jira, Docker, etc.)

Ways To Stand Out From The Crowd (Bonus):

Demonstrated experience of optimization workloads with GPU technology.

Significant deep learning experience with healthcare data and published record of thought leadership in a technical area or industry segment.

Experience in the application of data science methods to drug discovery

Experience with generative algorithms and explainable AI. Expertise in ADME/PK is a plus.

Proven experience working on or creating working ML/DL models for medical, biological, and life sciences datasets (minimum of 2 major projects).

Expertise in healthcare or life science and the processing of healthcare data.

Understanding of how to apply deep learning or machine learning into the healthcare process.

Sound knowledge of probability theory or statistics is a definite plus.

 Note: If you are interested please revert along with your updated resume and below mentioned details:

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