As a Data Scientist / Machine Learning Engineer at our company, your primary focus will be to develop and implement high-impact ML solutions that directly impact key business metrics such as revenue, customer experience, and risk mitigation. You will collaborate closely with Product and Engineering teams to identify opportunities, build models, and deploy them at scale. Some key responsibilities include:

• Partnering with Product teams to identify high-impact ML use cases linked to business outcomes
• Building and deploying ML models for pricing and discount optimization, fraud detection, and risk modeling
• Developing and productionizing models using APIs, CI/CD pipelines, and Docker
• Working on cloud platforms such as AWS or GCP for scalable ML deployment
• Building monitoring systems to track model performance, data drift, and business KPIs
• Collaborating with engineering teams to ensure scalable implementation

Qualifications required for this role include:

• 3+ years of experience as a Data Scientist / Machine Learning Engineer
• Solid programming skills in Python
• Strong understanding and hands-on experience with classical ML algorithms (regression, decision trees, gradient boosting)
• Hands-on experience in at least 2+ ML use cases, particularly in fraud/risk modeling
• Strong experience in NLP, including feature extraction from unstructured data
• Experience with productionizing ML models using APIs, Docker, and CI/CD
• Hands-on exposure to AWS/GCP environments

Please note that we are specifically looking for candidates with hands-on model deployment experience and those who have worked on real-world ML problems with direct business impact. This role offers you the opportunity to build and scale production-grade ML systems that drive business success. As a Data Scientist / Machine Learning Engineer at our company, your primary focus will be to develop and implement high-impact ML solutions that directly impact key business metrics such as revenue, customer experience, and risk mitigation. You will collaborate closely with Product and Engineering teams to identify opportunities, build models, and deploy them at scale. Some key responsibilities include:

• Partnering with Product teams to identify high-impact ML use cases linked to business outcomes
• Building and deploying ML models for pricing and discount optimization, fraud detection, and risk modeling
• Developing and productionizing models using APIs, CI/CD pipelines, and Docker
• Working on cloud platforms such as AWS or GCP for scalable ML deployment
• Building monitoring systems to track model performance, data drift, and business KPIs
• Collaborating with engineering teams to ensure scalable implementation

Qualifications required for this role include:

• 3+ years of experience as a Data Scientist / Machine Learning Engineer
• Solid programming skills in Python
• Strong understanding and hands-on experience with classical ML algorithms (regression, decision trees, gradient boosting)
• Hands-on experience in at least 2+ ML use cases, particularly in fraud/risk modeling
• Strong experience in NLP, including feature extraction from unstructured data
• Experience with productionizing ML models using APIs, Docker, and CI/CD
• Hands-on exposure to AWS/GCP environments

Please note that we are specifically looking for candidates with hands-on model deployment experience and those who have worked on real-world ML problems with direct business impact. This role offers you the opportunity to build and scale production-grade ML systems that drive business success.