Data Scientist - R/Python/Java (4-7 yrs)

We are seeking an experienced Data Scientist to lead and support the design, development, and implementation of data mining models, data analysis frameworks, and predictive analytics solutions.

The ideal candidate will have deep expertise in data science techniques and tools, hands-on experience working with large and complex datasets, and the ability to extract actionable insights to influence business outcomes.

Key Responsibilities :

- Design, build, and implement robust data mining architectures, algorithms, and machine learning models to extract insights from large-scale structured and unstructured datasets.

- Develop and automate end-to-end data pipelines for analysis and reporting.

- Use exploratory data analysis (EDA) and statistical methods to identify trends, patterns, anomalies, and correlations within large datasets.

- Collaborate with cross-functional teams including data engineers, analysts, product owners, and business stakeholders to develop data-driven solutions.

- Implement and refine predictive and prescriptive models to solve real-world business problems.

- Develop data visualizations and dashboards to effectively communicate findings to both technical and non-technical audiences.

- Continuously evaluate and improve data science methodologies, processes, and tools.

Required Skills & Qualifications :

- 3+ years of experience working with programming languages such as Python, R, Java

- 2+ years of hands-on experience with data visualization tools (e.g., Tableau, Power BI, matplotlib, seaborn, D3.js)

- 2+ years of experience with relational or NoSQL databases

- Strong grasp of statistical modeling, hypothesis testing, and machine learning algorithms (supervised and unsupervised)

- Experience with tools/frameworks like scikit-learn, TensorFlow, PyTorch, Spark MLlib is a plus.

Preferred Qualifications :

- Bachelors or Masters degree in Computer Science, Data Science, Statistics, Mathematics, or related field

- Experience working in domains like finance, healthcare, e-commerce, telecom, or manufacturing

- Exposure to cloud platforms like AWS, Azure, or GCP for data processing and model deployment