Data Scientist

Overview

Technical Skills

Programming Languages

- Proficiency in Python, with a focus on data processing libraries (e.g., Pandas).

- Familiarity with SQL for querying and managing relational databases.

Data Processing and Big Data

- Experience with Pandas, including optimization techniques such as:

- Chunk size to handle large datasets in memory-efficient batches.

- Basic understanding of PySpark for distributed data processing, including:

- Working with DataFrames for scalable data manipulations.

- Using Spark SQL for querying large datasets.

- Optimizing jobs using partitioning and caching.

Data Pipelines

- Basic understanding of ETL/ELT concepts.

- Exposure to pipeline tools like Apache Airflow or Luigi.

Databases and Optimization

- Familiarity with relational databases (e.g., MySQL, PostgreSQL).

- Techniques to improve database throughput, such as:

- Batch inserts for efficient data loading.

- Query optimization using indexing and partitioning.

- Managing database connections effectively to minimize latency.

- Basic understanding of NoSQL databases (e.g., MongoDB).

Cloud Fundamentals

- Basic knowledge of cloud platforms like AWS, Azure, or Google Cloud (e.g., S3, Lambda, BigQuery).

- Familiarity with distributed storage concepts.

Version Control and CI/CD

- Knowledge of Git/GitHub for collaboration and version control.

Data Modeling

- Basic understanding of relational and dimensional data modeling.

Analytical and Problem-Solving Skills

- Strong analytical skills to optimize data pipelines and troubleshoot performance bottlenecks.

- Basic understanding of distributed computing principles to scale data workflows.

Soft Skills

- Eagerness to learn and adapt to new tools and technologies.

- Clear communication and collaboration skills for working with cross-functional teams.

- Strong attention to detail for ensuring data quality and accuracy.

Education and Experience

- Bachelor’s degree in Computer Science, Data Science, or a related field.

- Hands-on experience with academic or personal projects involving large datasets.

- Familiarity with PySpark optimizations like: ?

- Using repartition() and coalesce() for better resource utilization.

- Understanding lazy evaluation and its role in Spark job execution.

- Awareness of visualization tools like Tableau or Power BI.

- Exposure to basic cloud certifications (e.g., AWS Certified Cloud Practitioner).

Job Type: Full-time

Pay: ?30,000.00 - ?40,000.00 per month

Schedule:

• Day shift

Education:

• Bachelor's (Required)

Experience:

• total work: 3 years (Required)

Location:

• Gurugram, Haryana (Required)

Work Location: In person