DataZymes Analytics is a cutting-edge and innovative data analytics company working with Life Sciences clients that empowers businesses and organizations to unlock the true potential of their data. Our mission is to harness the power of data and transform it into actionable insights, enabling our clients to make informed decisions and gain a competitive edge in today's data-driven world.
With a team of highly skilled data scientists, analysts, and engineers, DataZymes Analytics is committed to delivering top-notch solutions tailored to the unique needs of each client. Aided by our suite of cutting-edge products and accelerators, we are helping our Pharma clients on their Data to Insights journey. In a short span, we have built a high-performance team in focused practice areas, built digital-enabled solutions, and are working with some marquee names in the US healthcare industry.
What sets us apart is our dedication to accuracy, efficiency, and innovation. We utilize the latest technologies and industry best practices to ensure the highest quality of work and the fastest turnaround times. Moreover, our agile approach allows us to adapt quickly to changing requirements and deliver exceptional results within the most demanding timelines.
Job Description- 3+ years of work experience in Python programming for AI/ML, deep learning, and Generative AI model development
- Proficiency in TensorFlow/PyTorch, Hugging Face Transformers and Langchain libraries
- Hands-on experience with NLP, LLM prompt design and fine-tuning, embeddings, vector databases and agentic frameworks
- Strong understanding of ML algorithms, probability and optimization techniques
- 6+ years of experience in deploying models with Docker, Kubernetes, and cloud services (AWS Bedrock, SageMaker, GCP Vertex AI) through APIs, and using MLOps and CI/CD pipelines
- Familiarity with retrieval-augmented generation (RAG), cache-augmented generation (CAG), retrieval-integrated generation (RIG), low-rank adaptation (LoRA) fine-tuning
- Ability to write scalable, production-ready ML code and optimized model inference
- Experience with developing ML pipelines for text classification, summarization and chat agents
- Prior experience with SQL and noSQL databases, and Snowflake/Databricks
Skills Required
- 3+ years of experience in Python programming for AI/ML
- Experience with TensorFlow/PyTorch, Hugging Face Transformers and Langchain
- Hands-on experience with NLP, LLM prompt design, and fine-tuning
- Strong understanding of ML algorithms and optimization techniques
- 6+ years of experience in deploying models with Docker, Kubernetes, cloud services
- Familiarity with retrieval-augmented generation and related techniques
- Ability to write scalable, production-ready ML code
- Experience with developing ML pipelines for text classification and chat agents
- Prior experience with SQL, noSQL databases, Snowflake/Databricks
What We Do
Catalyzing Innovation in Pharma with Data-driven Digital Products. The idea of DataZymes germinated with the realization that Pharma Commercial teams had few alternatives to the antiquated and inefficient solutions offered by traditional consulting and technology companies. Already a laggard in analytical maturity, the Pharma industry had been facing challenges to adapt to a Big Data world. We saw that the products offered by technology companies were too rigid and generic to handle novel problems. The custom solutions offered by consulting organizations took too long to deploy and required many services to maintain and improve. We felt the need for a different approach to finding solutions and we knew it would take a different kind of company to build it. That’s why DataZymes. We’re focused on creating the world’s best user experience for working with data, one that empowers people to ask and answer complex questions without requiring them to master querying languages, statistical modeling, or the command line. To achieve this, we are building platforms for integrating, managing, and securing data on top of which we layer applications for fully interactive machine-driven, human-assisted analysis.
