At a Glance
- Tasks: Join a team of AI experts to innovate and optimise business decisions.
- Company: JPMorgan, a global leader in financial services with a focus on diversity.
- Benefits: Competitive salary, diverse work environment, and opportunities for professional growth.
- Other info: Dynamic role blending research and engineering with excellent career advancement potential.
- Why this job: Work at the cutting edge of AI in finance and make a real impact.
- Qualifications: Masters or PhD in a quantitative field; experience with machine learning and Python.
The predicted salary is between 70000 - 90000 £ per year.
Join a high performing team of applied AI experts to drive innovation and new capabilities in the Commercial & Investment Bank.
As an Applied AI / ML Senior Associate Machine Learning Engineer in the Applied AI ML team at JPMorgan Commercial & Investment Bank, you will be at the forefront of combining cutting-edge AI techniques with the company's unique data assets to optimize business decisions and automate processes. You will have the opportunity to advance the state-of-the-art in AI as applied to financial services, leveraging the latest research from fields of Natural Language Processing, Computer Vision, and statistical machine learning. You will be instrumental in building products that automate processes, help experts prioritize their time, and make better decisions. We have a growing portfolio of AI-powered products and services and increasing opportunity for re-use of foundational components through careful design of libraries and services to be leveraged across the team. This role offers a unique blend of scientific research and software engineering, requiring a deep understanding of both mindsets.
- Build robust Data Science capabilities which can be scaled across multiple business use cases.
- Collaborate with software engineering team to design and deploy Machine Learning services that can be integrated with strategic systems.
- Research and analyse data sets using a variety of statistical and machine learning techniques.
- Communicate AI capabilities and results to both technical and non-technical audiences.
Qualifications:
- Masters or PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Statistics.
- Solid understanding of fundamentals of statistics, optimization and ML theory. Experience monitoring, maintaining, enhancing existing models over an extended time period.
- Extensive experience with PyTorch and related data science Python libraries (e.g. pandas).
- Experience of containerising applications or models for deployment (Docker).
- Experience with one of the major public cloud providers (Azure, AWS, GCP).
- Experience of big data technologies.
- Track record of developing, deploying business critical machine learning models.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs.
StudySmarter Expert Advice🤫
We think this is how you could land Associate Machine Learning Engineer in London
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We think you need these skills to ace Associate Machine Learning Engineer in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at J.P. Morgan, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at J.P. Morgan. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at J.P. Morgan
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at J.P. Morgan!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
