Senior Associate - I&A (Data Science - Predictive Analytics)

As a Data Scientist Senior Associate at Chryselys, you will leverage advanced analytics, machine learning, and AI to transform complex datasets into actionable insights that drive strategic decision-making for clients in the pharmaceutical and healthcare industries. You will work closely with cross-functional teams to develop predictive models, optimize operations, and deliver innovative solutions that address critical business challenges. Key Responsibilities: - Design, develop, and implement machine learning models and algorithms to analyze pharmaceutical datasets in global markets and provide actionable insights. - Utilize advanced statistical techniques and data science methodologies to drive insights from complex data sources. - Build and maintain data pipelines and scalable machine learning models on Data Lake architecture, leveraging cloud-based platforms such as AWS (Redshift, S3, SageMaker, etc.). - Collaborate with cross-functional teams to translate business problems into data science solutions. - Create and present data visualizations, dashboards, and reports using tools like PowerBI, Tableau, Qlik, QuickSight, and ThoughtSpot to communicate findings and recommendations to clients. - Conduct exploratory data analysis, data profiling, and feature engineering to prepare datasets for predictive modelling. - Evaluate model performance, optimize algorithms, and ensure robustness and accuracy in predictions. - Stay current with the latest advancements in data science, machine learning, and AI, and apply them to ongoing projects. Qualifications Required: - Education: Bachelor's or master's degree in data science, statistics, computer science, engineering, or a related quantitative field with a strong academic record. - Experience: 2-5 years of experience in data science, particularly in the pharmaceutical or healthcare industry, working with key datasets like Sales, Claims, and Payer data. - Skills: - Proficiency in programming languages such as Python and R, with a deep understanding of libraries like TensorFlow, Scikit-learn, and Pandas. - Strong experience with SQL and cloud-based data processing environments such as AWS (Redshift, Athena, S3), and experience with Jupyter Notebooks/SageMaker. - Demonstrated ability to build data visualizations and communicate insights through tools like PowerBI, Tableau, Qlik, QuickSight, or similar. - Strong analytical skills, with experience in hypothesis testing, A/B testing, and statistical analysis. - Ability to manage multiple projects, prioritize tasks, and meet deadlines in a fast-paced environment. - Excellent communication and presentation skills, with the ability to explain complex data science concepts to non-technical stakeholders. - A strong problem-solving mindset, with the ability to adapt and innovate in a dynamic consulting environment. As a Data Scientist Senior Associate at Chryselys, you will leverage advanced analytics, machine learning, and AI to transform complex datasets into actionable insights that drive strategic decision-making for clients in the pharmaceutical and healthcare industries. You will work closely with cross-functional teams to develop predictive models, optimize operations, and deliver innovative solutions that address critical business challenges. Key Responsibilities: - Design, develop, and implement machine learning models and algorithms to analyze pharmaceutical datasets in global markets and provide actionable insights. - Utilize advanced statistical techniques and data science methodologies to drive insights from complex data sources. - Build and maintain data pipelines and scalable machine learning models on Data Lake architecture, leveraging cloud-based platforms such as AWS (Redshift, S3, SageMaker, etc.). - Collaborate with cross-functional teams to translate business problems into data science solutions. - Create and present data visualizations, dashboards, and reports using tools like PowerBI, Tableau, Qlik, QuickSight, and ThoughtSpot to communicate findings and recommendations to clients. - Conduct exploratory data analysis, data profiling, and feature engineering to prepare datasets for predictive modelling. - Evaluate model performance, optimize algorithms, and ensure robustness and accuracy in predictions. - Stay current with the latest advancements in data science, machine learning, and AI, and apply them to ongoing projects. Qualifications Required: - Education: Bachelor's or master's degree in data science, statistics, computer science, engineering, or a related quantitative field with a strong academic record. - Experience: 2-5 years of experience in data science, particularly in the pharmaceutical or healthcare industry, working with key datasets like Sales, Claims, and Payer data. - Skills: - Proficiency in programming languages such as Python and R, with a deep understanding of libraries like TensorFlow, Scikit-learn, and Pandas. - Strong experience with SQL and cloud-based data