Senior Data Scientist

Yugal Tech Academy; New York (NY); On-site; USD 120,000 - 150,000; Full time

Job summary

A leading financial institution is seeking a Senior Data Scientist responsible for designing and deploying advanced machine learning models. You'll collaborate with teams to understand use cases such as fraud detection and credit risk assessment. The ideal candidate has expertise in Python and data science libraries, experience with large datasets, and knowledge of machine learning frameworks. This role involves working on diverse datasets and ensuring models are production-ready, while also creating insights through dashboards and visualizations.

Qualifications

• Strong expertise in Python and data science libraries like Pandas and NumPy.
• Experience with machine learning frameworks like TensorFlow or PyTorch preferred.
• Hands-on experience with large datasets and SQL for data manipulation.
• Solid understanding of statistics and probability.
• Familiarity with MLOps practices and cloud platforms.

Responsibilities

• Design and deploy machine learning models for financial operations.
• Collaborate with stakeholders to understand use cases.
• Work with large-scale structured and unstructured datasets.
• Build machine learning models using various techniques.
• Ensure model deployment and monitor performance.

Skills

Python

Data science libraries (Pandas, NumPy, Scikit-learn, Matplotlib)

Machine learning frameworks (TensorFlow, PyTorch)

SQL

Problem-solving skills

Attention to detail

Tools

Spark

Hadoop

AWS

Azure

GCP

Job description

As a Senior Data Scientist at JPMorgan Chase, you will be responsible for designing and deploying advanced machine learning models that support critical financial operations such as fraud detection, credit risk assessment, customer segmentation, and predictive analytics. This role requires a deep understanding of both data science techniques and real-world financial systems, where accuracy, scalability, and reliability are essential.

Your work will begin with problem definition. You will collaborate with stakeholders from business, risk, and engineering teams to understand specific use cases such as predicting fraudulent transactions, identifying high-risk loan applicants, or optimizing customer engagement strategies. Based on these requirements, you will design end-to-end data science solutions.

You will work extensively with large-scale structured and unstructured datasets, including transaction logs, customer profiles, behavioral data, and external financial data sources. Data preprocessing will be a key part of your role, involving cleaning, normalization, feature engineering, and handling missing or inconsistent data.

Once the data is prepared, you will build machine learning models using techniques such as regression, classification, clustering, and ensemble methods. You may also work with deep learning models for more complex use cases. Model selection, hyperparameter tuning, and evaluation will be part of your daily workflow.

A critical aspect of this role is model deployment. You will not just build models but also ensure they are production-ready. This involves working with engineering teams to deploy models via APIs, batch pipelines, or streaming systems. You will also monitor model performance in real time, detect drift, and retrain models when necessary.

You will be expected to create clear and actionable insights from data. This includes building dashboards, reports, and visualizations that help business teams make informed decisions. Communication is key — you must be able to explain complex models in simple terms.

You will also ensure that all models comply with regulatory requirements and ethical standards, especially in financial decision-making systems.

Job Requirements

To succeed in this role, you must have strong expertise in Python and data science libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib. Experience with machine learning frameworks such as TensorFlow or PyTorch is highly preferred.

You should have hands-on experience working with large datasets and be comfortable using SQL for data extraction and manipulation. Experience with big data tools such as Spark or Hadoop is a strong advantage.

A solid understanding of statistics and probability is required. You should be comfortable with concepts such as hypothesis testing, distributions, regression analysis, and model evaluation metrics.

Experience in financial domain problems such as fraud detection, risk modeling, or customer analytics is highly valuable.

You should also have experience deploying models into production environments. Familiarity with cloud platforms such as AWS, Azure, or GCP is expected.

Knowledge of MLOps practices, including version control, CI/CD pipelines, and monitoring tools, is important.

Strong problem-solving skills, attention to detail, and the ability to work in a fast-paced environment are essential.