Associate- Referral - Decision Science / Data Science
This Requisition is for the Employee Referral Campaign.
We are seeking high-energy, driven, and innovative Data Scientists to join our Data Science Practice to develop new, specialized capabilities for Axtria, and to accelerate the company’s growth by supporting our clients’ commercial & clinical strategies.
Be an Individual Contributor to the Data Science team and solve real-world problems using cutting-edge capabilities and emerging technologies.
Help clients translate the business use cases they are trying to crack into data science solutions. Provide genuine assistance to users by advising them on how to leverage Dataiku DSS to implement data science projects, from design to production.
Data Source Configuration, Maintenance, Document and maintain work-instructions.
Deep working on machine learning frameworks such as TensorFlow, Caffe, Keras, SparkML
Expert knowledge in Statistical and Probabilistic methods such as SVM, Decision-Trees, Clustering
Expert knowledge of python data-science and math packages such as NumPy , Pandas, Sklearn
Proficiency in object-oriented languages (Java and/or Kotlin),Python and common machine learning frameworks(TensorFlow, NLTK, Stanford NLP, Ling Pipe etc
Data Scientist: 3-5 years of relevant experience in advanced statistical and mathematical models and predictive modeling using Python. Experience in the data science space prior relevant experience in Artificial intelligence and machine Learning algorithms for developing scalable models supervised and unsupervised techniques like NLP and deep Learning Algorithms. Ability to build scalable models using Python, R-Studio, R Shiny, PySpark, Keras, and TensorFlow. Experience in delivering data science projects leveraging cloud infrastructure. Familiarity with cloud technology such as AWS / Azure and knowledge of AWS tools such as S3, EMR, EC2, Redshift, and Glue; viz tools like Tableau and Power BI. Relevant experience in Feature Engineering, Feature Selection, and Model Validation on Big Data. Knowledge of self-service analytics platforms such as Dataiku/ KNIME/ Alteryx will be an added advantage.
ML Ops Engineering: 3-5 years of experience with MLOps Frameworks like Kubeflow, MLFlow, Data Robot, Airflow, etc., experience with Docker and Kubernetes, OpenShift. Prior experience in end-to-end automated ecosystems including, but not limited to, building data pipelines, developing & deploying scalable models, orchestration, scheduling, automation, and ML operations. Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS, MS Azure, or GCP). Programming languages like Python, Go, Ruby, or Bash, a good understanding of Linux, knowledge of frameworks such as Keras, PyTorch, TensorFlow, etc. Ability to understand tools used by data scientists and experience with software development and test automation. Good understanding of advanced AI/ML algorithms & their applications.