PhonePe Entry Level Jobs in Bangalore | New Vacancy 2024

PhonePe Entry Level Jobs in Bangalore: PhonePe is hiring freshers for Data Scientist roles in Bangalore. The function requires strong analytical capabilities, talent in programming languages like Python, and knowledge of machine-getting algorithms. Candidates have to have a relevant educational historical past in Computer Science, Mathematics, or a related subject.

Job Role: Data Scientist
Company: PhonePe
Location: Bengaluru, Karnataka
Experience: 0 – 7 years
Qualification: Bachelor’s or Master’s degree
Employment Type: Full Time, Permanent

About Company:

PhonePe is a leading Indian virtual payments platform, offering services like cash transfers, invoice payments, cellular recharges, and online shopping. Founded in 2015 and situated in Bangalore, it offers a consumer-friendly app for seamless transactions and monetary offerings. It operates at the UPI (Unified Payments Interface) platform and partners with numerous traders, permitting users to make payments and get entry to financial merchandise.

Job Description: PhonePe Entry Level Jobs in Bangalore

PhonePe is in search of a passionate and professional Data Scientist with 3+ years of revel in to sign up for their dynamic team. The ideal candidate could have a robust basis in gadget mastering and be capable of enforcing algorithms at scale. Expertise in Natural Language Processing (NLP) and Computer Vision (CV) might be effective but now not obligatory. The function would require you to increase contemporary gadget getting-to-know models to clear up complex commercial enterprise challenges, collaborate with go-useful teams, and speak statistics-driven insights effectively.

Key Responsibilities:

Model Development and Implementation:

Develop and put in force system learning fashions, each offline batch model, and real-time online fashions, ensuring they can scale and perform successfully.
Work on area computing fashions to handle data processing on the supply of facts generation.

Data Analysis and Insights Extraction:

Analyze large and complex datasets to extract meaningful insights that could drive important commercial enterprise choices.
Apply statistical strategies and gadget-getting-to-know algorithms to find styles, traits, and correlations in information.

Collaboration and Problem-Solving:

Collaborate with business stakeholders and cross-purposeful groups to understand commercial enterprise problems and advise statistics-driven solutions.
Translate complex analytical findings into actionable guidelines and speak them efficiently to non-technical audiences.

Deployment and Optimization:

Ensure machine getting-to-know fashions are carried out and deployed effectively in production environments.
Continuously display model performance, and high-quality models, and optimize them for higher accuracy and efficiency.

Documentation and Reporting:

Document methodologies, approaches, and code for the fashions you build, making ensure easy reproducibility.
Report findings and updates to stakeholders, highlighting capability areas for development.

Required Qualifications:

Educational Background: A Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field is required.

Experience:

At least 3 years of experience as a Data Scientist or in a similar role where machine learning and data analysis played a central part.
Proven ability to work with large datasets and implement machine learning algorithms for real-world applications.

Technical Skills:

Programming Languages: Proficiency in Python (required) and SQL (required). A basic know-how of Java is favored.
Strong foundation in conventional device studying algorithms (e.G., regression, category, clustering).
Knowledge of Natural Language Processing (NLP) and Computer Vision (CV) principles and algorithms is a plus.
Familiarity with large records technology, in particular PySpark.

Mathematical and Statistical Expertise:

Solid expertise of records, linear algebra, and chance concept.
Strong analytical thinking and trouble-solving abilties to layout progressive solutions the usage of statistics.