Experience
: 5.00 + years
Salary
: Confidential (based on experience)
Expected Notice Period
: 30 Days
Shift
: (GMT+05:30) Asia/Kolkata (IST)
Opportunity Type
: Hybrid ()
Placement Type
: Full Time Permanent position(Payroll and Compliance to be managed by: An AI-powered fraud prevention platform)
(*Note: This is a requirement for one of Uplers' client - An AI-powered fraud prevention platform)What do you need for this opportunity?Must have skills required:Deep Learning, E-commerce/Shopify Domain., Data Scientist, Machine Learning, ML, Applied Scientist, Fraud Data Scientist, Payments, Transaction fraud, fraud detection, fraud prevention, Transaction risk, Chargeback, Python, Scikit-learn, PyTorch, TensorFlow, SQL, pandas, OpenAI, LangChain, Hugging Face, LLM, rag, payment, Financial Services, Fintech, MLOps, Production mlAn AI-powered fraud prevention platform is Looking for:
Data Scientist / MLE (Fraud & Payments)
About PA
PA is a Shopify-native return-fraud and order-risk decisioning platform that reinvents the post-purchase experience for retailers. We help merchants catch return abuse, empty-box (FTID) fraud, claim abuse, and payment fraud in real time — making high-stakes approve/decline decisions on live order and return traffic. By identifying and preventing abusive behaviour while rewarding loyal customers, we turn returns from a cost centre into a driver of loyalty and profit.
The Role
We''re looking for a hands-on Data Scientist / MLE to help us build and deploy intelligent data systems. You''ll work closely with the Head of Machine Learning to clean, analyse, and model data, and to operationalise models and customer datasets in production.
Prior experience working in payment fraud or the payments domain is a must — you should already be comfortable with the data, risk signals, and problem patterns that come from fraud and transactional environments.
Our core work lies at the intersection of data intelligence, risk modelling, and identity resolution. You''ll build machine-learning systems that understand user, order, and return behaviour across digital commerce, working on problems like:
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Identity Resolution: Connecting entities and related data points into unified customer identities.
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Data Segregation and Clustering: Cleaning, parsing, and geo-clustering data to reveal underlying structure.
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Abuse and Fraud Detection: Modelling behavioural and transactional risk signals to detect fraudulent activity.
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Anomaly Detection and Scoring Pipelines: Identifying irregular patterns across large customer datasets.
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Operational ML Systems: Deploying scoring APIs, automated feature stores, and monitoring pipelines at scale.
What You''ll Do
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Perform data analysis, feature engineering, and exploratory insights across business datasets.
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Build and deploy ML models (classification, regression, ranking, anomaly detection, etc.).
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Fine-tune and integrate LLMs (OpenAI, Hugging Face, LangChain, RAG pipelines).
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Support model deployment in cloud / API environments (Docker, GCP).
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Create dashboards, reports, and validation pipelines for model and data monitoring.
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Collaborate with engineering on scalable data workflows and inference pipelines.
What We''re Looking For
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Hands-on experience in payment fraud or the payments domain is required — including exposure to transactional risk signals, fraud detection, or related risk-modelling work.
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Python (NumPy, Pandas, Scikit-learn, TensorFlow or PyTorch).
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Experience with LLM frameworks (Hugging Face, LangChain, OpenAI API).
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Basic knowledge of MLOps (Docker, CI/CD, API serving, Git).
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SQL / NoSQL data querying familiarity with cloud data environments.
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Strong analytical mindset comfortable with statistics and data visualisation (e.g., Plotly, Seaborn).
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Curious, pragmatic problem solver who enjoys taking models from notebook to production.
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Able to learn quickly and adapt in a fast-moving AI environment.
Nice to Have
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Exposure to RAG, prompt-tuning, or lightweight model fine-tuning.
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Understanding of model monitoring and data drift detection.
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Understanding of deep learning and graph data structures.
Why PA
You''ll work on a high-stakes, real-time product where your work directly protects revenue, alongside a small, senior team with a short path from idea to production.
How to apply for this opportunity?
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Step 1: Click On Apply! And Register or Login on our portal.
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Step 2: Complete the Screening Form & Upload updated Resume
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Step 3: Increase your chances to get shortlisted & meet the client for the Interview!
About Uplers:
Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement.
(Note: There are many more opportunities apart from this on the portal. Depending on the assessments you clear, you can apply for them as well).
So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!
