PHARMAFull-time
Data Scientist - Data Modeling/analytics
By Admin · Data Science, Analytics & AI
HYDERABAD (IN)ONSITEPOSTED: 8/27/2026
PHARMAFull-time
HIRING ORGANIZATIONAMGENBiotechnology / Pharmaceuticals
ROLE DEFINITION & RESPONSIBILITIES
Role Overview
Senior Associate Data Scientist role within Amgen's Information Systems career category, based in Hyderabad, India.
Full-time, on-site position focused on advanced machine learning, statistical modeling, forecasting, analytics, and AI-enabled decision-support solutions.
The role supports statistical, Bayesian, causal, and machine learning models that enhance forecasting and quantify uncertainty for business decision-making.
The position involves complex datasets, modern analytical tools, model development, deployment support, MLOps, simulation, scenario analysis, and AI-enabled automation.
Core Responsibilities
Analyze large and complex datasets using statistical modeling, forecasting, and analytics techniques to generate insights for business decisions.
Support development of simulation and scenario-analysis capabilities to improve understanding of data and potential outcomes.
Contribute throughout the end-to-end modeling lifecycle, including business-problem framing, exploratory data analysis, feature engineering, model development, validation, deployment support, monitoring, and explainability.
Work with senior team members to evaluate and apply new forecasting, data-science, and AI tools and methodologies to business problems.
Communicate analytical findings and model results clearly to both technical and non-technical stakeholders.
Ensure models are trained using the latest available data and meet applicable SLA expectations.
Collaborate with global cross-functional teams on the AI tool roadmap.
Work within technical teams on development, deployment, and application of applied analytics, predictive analytics, and prescriptive analytics.
Apply hypothesis testing, machine learning, retrieval processes, statistical methods, and data-mining techniques to identify trends, create figures, and analyze relevant information.
Perform exploratory and targeted data analyses using descriptive statistics and other analytical methods.
Develop model and analytics experimentation and development pipelines leveraging MLOps.
Translate business needs into technical specifications, particularly for AI-driven automation and insights.
Develop and integrate custom applications, intelligent dashboards, and automated workflows incorporating AI capabilities to improve decision-making and operational efficiency.
Statistical & Machine Learning Expertise
Apply statistical modeling, forecasting, predictive analytics, and machine learning techniques.
Use hypothesis testing, descriptive statistics, experimental analysis, and data-mining methods.
Develop simulation and scenario-analysis models.
Work with complex datasets and identify trends and meaningful patterns.
Build models across exploration, feature engineering, development, validation, deployment, monitoring, and explainability stages.
MLOps & Model Lifecycle
Develop model and analytics experimentation pipelines using MLOps practices.
Support model deployment, monitoring, retraining, versioning, and performance management.
Ensure models use current data and satisfy service-level expectations.
Good-to-have exposure includes MLflow, Kubeflow, Airflow, Docker, Kubernetes, and CI/CD.
AI, Applications & Automation
Translate business requirements into technical and analytical solutions.
Build AI-enabled dashboards, automated workflows, and custom data applications.
Integrate AI capabilities into decision-support solutions.
Collaborate with global technical teams on AI-tool roadmaps and applied analytics initiatives.
Technical Requirements
Strong Python and SQL skills for data analysis, statistical modeling, and automation.
Hands-on machine learning, predictive analytics, and forecasting experience.
Solid statistical knowledge, including hypothesis testing, descriptive statistics, and experimental analysis.
Experience working with large and complex datasets through data-mining and analytical techniques.
Familiarity with the complete model lifecycle and MLOps practices.
Experience with simulation or scenario-analysis models.
Ability to convert business problems into technical requirements and analytical solutions.
Experience developing AI-enabled dashboards, automated workflows, or data applications.
Strong stakeholder communication capabilities for explaining analytical insights and model outcomes.
Experience working within global, cross-functional technical teams.
Good-to-Have Technical Skills
MLOps tools such as MLflow, Kubeflow, and Airflow.
DevOps tools such as Docker, Kubernetes, and CI/CD.
Python machine-learning libraries including TensorFlow, PyTorch, and Scikit-learn.
Data engineering and pipeline development.
Natural Language Processing (NLP), text analysis, and sentiment analysis.
Time-series analysis for forecasting and trend analysis.
AWS, Azure, or Google Cloud experience.
Databricks for data analytics and MLOps.
Education & Experience
Bachelor's or Master's degree in Computer Science, Statistics, or another STEM discipline.
Requires a minimum of 4 years and maximum of 7 years of Information Systems experience according to the posting.
Preferred professional certifications include AWS Developer certification and Python/ML certifications.
Soft Skills & Working Style
Initiative to explore alternative technologies and approaches.
Ability to break complex problems into manageable components, document problem statements, and estimate required effort.
Excellent analytical and troubleshooting skills.
Strong verbal and written communication.
Ability to work effectively with global virtual teams.
High initiative and self-motivation.
Ability to manage multiple priorities successfully.
Team-oriented approach focused on achieving shared goals.
Business Impact
Build decision-support models that improve forecasting and planning.
Quantify uncertainty to support informed business decisions.
Develop AI-enabled automation and intelligent dashboards to increase efficiency.
Apply predictive and prescriptive analytics to complex business problems.
Help advance Amgen's technology-driven approach to healthcare and pharmaceutical innovation.
REQUIRED VERIFIED SKILLS
Python
SQL
Machine Learning
Predictive Analytics
Forecasting
Statistical Modeling
Hypothesis Testing
Descriptive Statistics
Experimental Analysis
Data Mining
Exploratory Data Analysis
Feature Engineering
Model Development
Model Validation
Model Deployment
Model Monitoring
Model Explainability
MLOps
Simulation Modeling
Scenario Analysis
AI-enabled Analytics
Large Dataset Analysis
Business-to-Technical Translation
AI Dashboards
Automated Workflows
Global Cross-functional Collaboration
COMPENSATION & REWARDS
Salary: Competitive / Disclosed upon candidate shortlisting
ORGANIZATION CONTEXT & CULTURE
Amgen is a leading global biopharmaceutical company committed to unlocking the potential of biology for patients suffering from serious illnesses. It discovers, develops, manufactures, and delivers innovative human therapeutics, focusing on areas with high unmet medical needs such as oncology, cardiovascular disease, bone disease, and inflammatory diseases.
TEAM SIZE: 25,000+ employees
INDUSTRY: Biotechnology / Pharmaceuticals
RECOMMENDED TRAINING
1/5
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