Client Services Lead

Candidate Profile

• Previous experience in building data science / algorithms based products is big advantage.
• Experience in handling healthcare data is desired.
  
  

• 7-9 years experience of industry experience in developing data science models and solutions.
• Able to quickly pick up new programming languages, technologies, and frameworks
• Strong understanding of data structures and algorithms
• Proven track record of implementing end to end data science modelling projects, providing the guidance and thought leadership to the team.
• Strong experience in a consulting environment with a do it yourself attitude.
  
  

• As a Data science lead you will be responsible to lead a team of analysts and data scientists / engineers and deliver end to end solutions for pharmaceutical clients.
• Is expected to participate in client proposal discussions with senior stakeholders and provide thought leadership for technical solution.
• Should be expert in all phases of model development (EDA, Hypothesis, Feature creation, Dimension reduction, Data set clean-up, Training models, Model selection, Validation and Deployment)
• Should have deep understanding of statistical & machine learning methods ((logistic regression, SVM, decision tree, random forest, neural network), Regression (linear regression, decision tree, random forest, neural network), Classical optimisation (gradient descent etc),
• Must have thorough mathematical knowledge of correlation/causation, classification, recommenders, probability, stochastic processes, NLP, and how to implement them to a business problem.
• Should be able to help implement ML models in a optimized , sustainable framework.
• Expected to gain business understanding in health care domain order to come up with relevant analytics use cases. (E.g. HEOR / RWE / Claims Data Analysis)
• Expected to keep the team up to date on latest and great in the world on ML and AI.
  
  

• Expert level proficiency in programming language Python/SQL.
• Working knowledge of Relational SQL and NoSQL databases, including Postgres, Redshift
• Extensive knowledge of predictive & machine learning models in order to lead the team in implementation of such techniques in real world scenarios.
• Working knowledge of NLP techniques and using BERT transformer models to solve complicated text heavy data structures.
• Working knowledge of Deep learning & unsupervised learning.
• Well versed with Data structures, Pre-processing , Feature engineering, Sampling techniques.
• Good statistical knowledge to be able analyse data.
• Exposure to open source tools & working on cloud platforms like AWS and Azure and being able to use their tools like Athena, Sagemaker, machine learning libraries is a must.
• Exposure to AI tools LLM models Llama (ChatGPT, Bard) and prompt engineering is added advantage
• Exposure to visualization tools like Tableau, PowerBI is an added advantage