Sr. Data Scientist

The ideal candidate's favorite words are learning, data, scale, and agility. You will leverage your strong collaboration skills and ability to extract valuable insights from highly complex data sets to ask the right questions and find the right answers. You are a data pro with deep statistical knowledge and analytical aptitude. You know how to make sense of massive amounts of data and gather deep insights. You will use statistics, data mining, machine learning, and deep learning techniques to deliver data-driven insights for clients. You are a thought leader with a commercial acumen, always on top of AI and ML trends. Responsibilities ● Analyse raw data: assessing quality, cleansing, structuring for downstream processing ● Design accurate and scalable prediction algorithms ● Collaborate with engineering team to bring analytical prototypes to production ● Generate actionable insights for business improvements

Qualifications

● Have worked on Physician segmentation, physician behavior modeling, drug prescribability, drug prescription churn, physician prioritization ● At least 2-4 years’ developing Machine Learning methods ● At least 2-5 year Deep learning experience ● Have worked on Medical claims data (patient medical journeys, medical insurance claims, patient ICD-9/10 code medical journey) ● Deep understanding of predictive modeling, machine-learning, clustering and classification techniques, and algorithms ● Fluency with Python, Database(MySQL/Postgres/MongoDB), Git versioning, Data Analysis Libraries(NumPy, Pandas, Statsmodels, Dask), Machine Learning Libraries( Scikit-learn),

Deep Learning Libraries(Tensorflow/Keras/Pytorch/fast.ai), Data Visualization Libraries(Matplotlib, Plotly) ● Familiarity with Big Data frameworks and visualization tools (Cassandra, Hadoop, Spark, Tableau) ● Familiarity with AWS or Azure

Optional · Team lead experience · Consulting background · clustering, auto-regression models, NER, topic modeling, object detection, semantics/instance segmentation, object tracking, · Good understanding of Word2vec, RNNs, Transformers, Bert, Resnet, MobileNet, Unet, Mask-RCNN, Siamese Networks, GradCam, image augmentation techniques, GAN, Tensorboard · Deployment - Flask, Tensorflow serving, Lambda functions, Docker