At a Glance
- Tasks: Lead data science projects, build predictive models, and consult on innovative solutions.
- Company: Dynamic financial software house with a relaxed, tech-driven culture.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Other info: Work in a calm atmosphere with an open dress code and a focus on innovation.
- Why this job: Join a cutting-edge team and make a real impact in the financial tech space.
- Qualifications: 2+ years in data science, strong consultancy skills, and proficiency in Python.
The predicted salary is between 63000 - 77000 £ per year.
Lead Data Science Consultant Permanent or Contract Quant Capital is urgently looking for Data Scientist / Machine Learning Consultant to join our high profile client.
My client is a well known financial software house, providing platforms and consultancy to the financial and insurance space.
The Data Science Consultant is responsible for performing predictive analytics and build models across credit risk, fraud, collections and marketing.
The successful Data Scientist will lead the team that will define the Big Data strategy for my client and will lead or participate in R&D projects in the digital space and identify new tools, methods and opportunities to profitably grow the business.
Building relationships with business SMEs and stakeholders to understand their business requirements.
Logical and physical modelling.
Extract data from business (who don’t always know where it is or what it does)Present data from various unclean sources Communicating innovative data-centric solutions and providing strong architectural designs to develop a workable solution.
Machine Learning Consultancy Documenting Provide expertise on Data management capabilities.
Analyse and model data concepts across the enterprise.
Consulting on the necessary data architecture, elaborating the requirements, business KPIs and other reporting or analytical outputs.
Data Science Consultants / Machine Learning MUST have: (the below also gives you an idea of stack)Implementation of machine learning-based or data science based solutions for CORPORATE clients.
Strong Business acumen and experience cross industry Delivery of ongoing/ production solutions, not simply one-off analyses Consultancy skills, including ability to shape ambiguous requirements into clearly defined projects Professional experience of more than 2 years Ability to client face and consult Strong Data Science background Strong understanding of probability and statistics Ideally Machine learning (experience of techniques such as linear regression, logistic regression, boosting, k NN)Ideally Python or another language Experience of one or more common frameworks (e. g.
Caffe, Theano, Torch, Tensorflow, scikit-learn)The environment is that of Facebook or Google, relaxed open with time to think and make the right decisions.
The atmosphere is calm and relaxed with an open dress code.
This is a role for techies, those who are motivated by the sharp end of technology and the possibility of making serious money doing something you are passionate about.
My client is based near Wimbledon, London If this sounds of interest then please apply directly.
Machine Learning, Data Science, python, Bubble visualisation Linux AWS Cloud Systems Infrastructure Linux Sys Admin Financial Tech STartup – Linux, Scripting, Automation, Kernel, No SQL DEVOPS Python Development Operations
Lead Data Science Consultant in London employer: Quant Capital
Quant Capital is an excellent employer for those looking to thrive in the fintech sector, offering a vibrant work culture that fosters innovation and collaboration. With substantial training and development opportunities, employees can enhance their skills while enjoying a flexible hybrid work model in the heart of London. Join us to be part of a forward-thinking team that values growth and cutting-edge technology.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Science Consultant in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Quant Capital!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Lead Data Science Consultant at Quant Capital.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Quant Capital.
✨Apply Directly through Our Website
When you find a suitable opening like Lead Data Science Consultant at Quant Capital, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Lead Data Science Consultant 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!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Quant Capital, 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 Quant Capital. 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 Quant Capital
✨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!
✨Showcase Your Projects
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 Quant Capital!
✨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.
