Data Scientist

Our Story
Our Story: Our client has a vision to transform patient experience in the underserved markets by bridging the gap which exists in primary healthcare. Our client is introducing a hybrid healthcare and fulfilment model that helps connect patients in underserved markets to healthcare providers and other key ecosystem players digitally and executing it through a robust 3-tier model of Hub, Spoke and a network of satellite health centres.
: A Data Scientist is responsible for collecting, cleaning, analyzing, and interpreting large volumes of data to extract actionable insights. Utilize statistical, mathematical, and computational techniques to solve business problems, improve processes, and support decision-making.
Key Responsibilities

• Data Collection and Cleaning: Gather and process data from various sources, ensuring data quality and consistency.
• Data Exploration: Explore and visualize data to understand patterns, trends, and anomalies.
• Statistical Analysis: Apply statistical methods to extract meaningful insights from data, such as hypothesis testing, regression analysis, and clustering.
• Machine Learning: Develop and implement machine learning models for predictive and prescriptive analytics. This includes feature engineering, model selection, and evaluation.
• Data Mining: Discover hidden patterns and knowledge within data using techniques like association rule mining and anomaly detection.

• Data Visualization: Create clear and informative data visualizations to communicate findings effectively to non-technical stakeholders.
• Model Deployment: Deploy machine learning models into production environments and monitor their performance.
• A/B Testing: Design and analyze A/B tests to evaluate the impact of changes or interventions.
• Domain Knowledge: Expertise in the healthcare domain and understand the context and nuances of the data. Knowledge on clinical decision support, healthcare process optimization, disease surveillance, drug discovery and development, patient outcome improvements and brand performance.
• Collaboration: Collaborate with cross-functional teams, including engineers, business analysts, and domain experts, to solve complex problems and drive data-driven decision-making.
  

Skills And Qualifications

• Data Analysis: Proficiency in data manipulation and analysis tools such as Python, R, or SQL.
• Machine Learning: Strong knowledge of machine learning algorithms and frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
• Statistics: Solid understanding of statistical concepts and their application.

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  Data Visualization: Proficiency in data visualization libraries (e.g., Matplotlib, Seaborn, D3.js) and tools (e.g., Apache Superset, Power BI).
• Big Data Tools: Familiarity with big data technologies like Hadoop and Spark is a plus.
• Programming: Ability to write code for data analysis and modeling in cloud environment
• Database Knowledge: Understanding of relational databases and NoSQL databases.
• Domain Expertise: Understanding of healthcare systems, medical terminology, and clinical processes is essential.
• Communication: Strong communication skills to convey complex findings to non-technical stakeholders.
• Problem Solving: Analytical and critical thinking skills to approach and solve complex business problems.
• Interdisciplinary Collaboration: Collaborate with healthcare providers, clinicians, data engineers, and researchers to address healthcare challenges and drive data-driven solutions.
  

Education And Experience
A bachelors degree in a relevant field (such as computer science, statistics, mathematics, or a related field) is typically required.
Relevant work experience in data analysis, machine learning, or a related field is often preferred. Entry-level positions may require internships in the relevant domain