Data Scientist

4 weeks ago

BangaloreAnywhere in IndiaMultiple Locations Talent Socio Full time

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.

- 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.

(ref:hirist.tech)

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