Data Scientist

• Data Collection and Preparation: Sourcing data from various internal and external sources (databases, APIs, web scraping), and cleaning, processing, and validating large structured and unstructured datasets for accuracy and completeness.
• Exploratory Data Analysis (EDA): Examining data to identify patterns, trends, relationships, and anomalies using statistical methods and data visualization techniques.
• Model Building and Implementation: Designing, building, training, and optimizing machine learning models and algorithms (e.g., classification, regression, clustering, neural networks) to forecast outcomes or automate processes.
• Communication and Collaboration: Working with cross-functional teams (e.g., product, marketing, engineering) to understand business needs and presenting complex results and recommendations to technical and non-technical stakeholders in a clear and compelling manner.
• Deployment and Monitoring: Collaborating with data and machine learning engineers to deploy models into production environments and developing processes to monitor and analyze model performance over time.
• Strategic Input: Advising leadership on data-driven strategies, conducting A/B testing, and identifying opportunities to leverage data for business growth and optimization.
• Required Skills and Qualifications A data scientist needs a blend of technical expertise, analytical thinking, and soft skills:
• Programming Languages: Proficiency in statistical programming languages such as Python and R, and database query languages like SQL. Statistics and Mathematics: Strong applied statistical skills (statistical tests, distributions, regression) and a solid understanding of linear algebra and multivariable calculus.
• Machine Learning: Experience with various machine learning techniques and algorithms (e.g., k-NN, Naive Bayes, Decision Forests, SVM) and relevant libraries (scikit-learn, TensorFlow, PyTorch).
• Data Visualization: Skill in using visualization tools like Tableau, Power BI, Matplotlib, or D3.js to create reports and dashboards.
• Big Data Technologies (optional but helpful): Familiarity with distributed data processing tools and platforms such as Hadoop, Spark, or cloud services (AWS, Azure, GCP).
• Education: Typically requires a bachelor's or master's degree in a quantitative field such as Computer Science, Statistics, Engineering, Mathematics, or Economics.
• Soft Skills: Strong problem-solving aptitude, critical thinking, business acumen, intellectual curiosity, and excellent communication skills are essential for success in this role.
• Hiring for freshers

Job Types: Full-time, Permanent, Fresher

Pay: ₹587,617.48 - ₹1,852,217.81 per year

Benefits:

• Leave encashment

Work Location: In person