Job Responsibilities

Craft winning proposals to grow the Data Science Practice.

Develop and operationalize scalable processes to deliver on large & complex client engagements.

Ensure profitable delivery and great customer experience – design the end to end solution, put together the right team and deliver as per established processes.

Build an A team – hire the required skills sets and nurture them in a supporting environment to develop strong delivery teams for the Data Science Practice.

Train and mentor staff and establish best practices and ways of working to enhance data science capabilities at Axtria. Operationalize an eco-system for continuous learning & development.

Write white papers, collaborate with academia and participate in relevant forums to continuously upgrade self knowledge & establish Axtria’s thought leadership in this space. Research, develop, evaluate, and optimize newly emerging algorithms and technologies for relevant use cases in pharma commercial & clinical space.

Education Bachelor Equivalent - OtherPG Diploma in ManagementWork Experience

Data Scientist : 5 - 10 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, 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 : 5 -10 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.

Gen AI : Minimum of 4-6 years develop, test, and deploy Python based applications on Azure/AWS platforms. Must have basic knowledge on concepts of Generative AI / LLMs / GPT. Deep understanding of architecture and work experience on Web Technologies. Python, SQL hands-on experience. Expertise in any popular python web frameworks e.g. flask, Django etc. Familiarity with frontend technologies like HTML, JavaScript, REACT. Be an Individual Contributor in the Analytics and Development team and solve real-world problems using cutting-edge capabilities and emerging technologies based on LLM/GenAI/GPT. Can interact with client on GenAI related capabilities and use cases.

Technical Competencies ML Data ScienceML OpsML engineeringPharmaPythonPySparkAWS Data PipelineAWS EMRAIMLNLPBehavioural Competencies OwnershipTeamwork & LeadershipTalent ManagementCultural FitMotivation to Learn and GrowProject Management