Data Scientist - Machine Learning

About Quantzig

Quantzig is a global analytics and advisory firm with offices in the US, UK, Canada, China, and India. we have assisted our clients across the globe with end-to-end advanced analytics, visual storyboarding , Machine Learning and data engineering solutions implementation for prudent decision making. We are a rapidly growing organization that is built and operated by high-performance champions.

Description

If you have what it takes to be the champion with business and functional skills to take ownership of an entire project end-to-end, help build a team with great work ethic and a drive to learn, you are the one were looking for. The clients love us for our solutioning capability, our enthusiasm and we expect you to be a part of our growth Description :

Experience : 3-6 years

Location : Bangalore (WFO)

Notice period : Overview :

Requirements

We are seeking an 3+ years of experienced Data Scientist with hands-on experience in Python, SQl, Machine Learning and Azure Databricks. The ideal candidate will have a proven track record in leveraging classic data science algorithms, advanced machine learning techniques to drive data-driven decision-making and Requirements :

• Programming and Tools : Expert in Python, SQL, Azure Databricks and relevant libraries/frameworks.
• Advanced ML Techniques : Experience in developing and deploying models for classification, clustering, ensemble modelling (e.g., Random Forest, XG Boost), and anomaly detection for fraud :
• Collaborate with stakeholders to understand business problems related to fraud detection and prevention, and translate them into actionable data science projects.
• Conduct thorough exploratory data analysis (EDA) to understand data characteristics, identify relevant features, and engineer new features to improve model performance.
• Develop and implement a variety of machine learning models, including classic algorithms and advanced techniques like ensemble methods and anomaly detection algorithms, specifically tailored for fraud analysis. Rigorously evaluate model performance using appropriate metrics.
• Deploy machine learning models into production environments using Azure Databricks or related deployment tools. Implement robust monitoring systems to track model performance and ensure stability.
• Design and implement scalable data pipelines and machine learning workflows within the Azure Databricks environment. Optimize code and models for performance.
• Clearly communicate technical findings, model performance, and actionable insights to both technical and non-technical audiences. Visualize results effectively to convey the impact of your work.
• Work closely with data engineers, analysts, and other team members to ensure seamless data integration and model deployment.
• Stay up-to-date with the latest advancements in machine learning, particularly in fraud detection and anomaly detection techniques, and explore opportunities to apply them to our challenges.
• Maintain comprehensive documentation of code, models, and deployment :
• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
• 3-6 years of hands-on experience as a Data Scientist.
• Expert-level proficiency in Python and SQL.
• Extensive experience working with Azure Databricks for data processing and machine learning.
• Proven track record of developing and deploying machine learning models for classification, clustering, ensemble modeling (Random Forest, XGBoost), and anomaly detection.
• Strong understanding of machine learning algorithms, statistical concepts, and model evaluation metrics.
• Experience in applying machine learning techniques specifically for fraud analysis or related anomaly detection problems is highly desirable.
• Excellent analytical and problem-solving skills with the ability to work with complex datasets.
• Strong communication and presentation skills.
• Ability to work independently and collaboratively within a team.
• Must be available for immediate joining and willing to work from our Bangalore office.
  
  

(ref:hirist.tech)