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- Tasks: Join our Applied AI team to develop and deploy ML/AI models using large-scale data.
- Company: Be part of a leading bank driving innovation through data science and AI.
- Benefits: Enjoy a hybrid work model with flexible office days and competitive pay rates.
- Why this job: Make a real impact by transforming data into actionable insights in a collaborative environment.
- Qualifications: Expertise in Python, SAS, SQL, and machine learning libraries is essential.
- Other info: Opportunity to work on long-term projects with cutting-edge technology.
The predicted salary is between 43200 - 72000 £ per year.
We are looking for a Data Scientist to help the Applied AI team in the Bank, holding expertise in machine learning and AI capabilities, working on ML/AI model development, evaluation and deployment based on large-scale data processing.
Long-term project London Hybrid - Max Three Days in the office Rate 550-600
Position Overview:
- Conducts strategic data analysis, identifies insights and implications and makes strategic recommendations, develops data displays that clearly communicate complex analysis.
- Collaborate with stakeholders across cross-functional teams to understand data needs, translate them into impactful data-driven solutions and deliver these in partnership with the technology team.
- Develop and integrate functionality to ensure adherence with best-practices in terms of data management and data governance.
- Mine and analyse data from various banking platforms to drive optimisation and improve data quality.
- Collaborate on design and implementation of workflow solutions that provide long-term scalability, reliability, and performance, and integration with reporting.
Required Skills:
- Expertise and hands-on experience in advanced programming using: SAS / Python / pySpark and SQL for data mining; additional experience and knowledge of Big Data tools preferred.
- Excellent Python programming skills, including experience with relevant analytical and machine learning libraries (e.g., pandas, polars, numpy, sklearn, TensorFlow/Keras, PyTorch, etc.), in addition to visualisation and API libraries (matplotlib, plotly, streamlit, Flask, etc).
- Understanding of Gen AI models, Vector databases, Agents, and follow the market trends. It is desirable to have hands-on experience on these.
- Substantial experience using tools for statistical modelling of large data sets.
- Some familiarity with data workflow management tools such as Airflow as well as big data technologies such as Apache Spark or other caching and analytics technologies.
- Expertise in model training, Statistics, model evaluation, deployment and optimisation, including RAG-based architectures.
Data Scientist employer: Synechron
Contact Detail:
Synechron Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist
✨Tip Number 1
Familiarise yourself with the specific tools and technologies mentioned in the job description, such as SAS, Python, and SQL. Make sure you can discuss your hands-on experience with these tools confidently during interviews.
✨Tip Number 2
Stay updated on the latest trends in AI and machine learning, particularly around Gen AI models and vector databases. Being able to discuss current market trends will show your passion and commitment to the field.
✨Tip Number 3
Prepare examples of past projects where you've successfully collaborated with cross-functional teams. Highlight how you translated data needs into actionable solutions, as this aligns closely with the role's requirements.
✨Tip Number 4
Practice explaining complex data analyses in simple terms. The ability to communicate insights clearly is crucial for this role, so consider rehearsing with friends or colleagues to refine your delivery.
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your expertise in machine learning, AI capabilities, and programming languages like Python and SQL. Include specific projects or experiences that demonstrate your skills in data analysis and model development.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the Data Scientist role and how your background aligns with the job requirements. Mention your experience with relevant tools and technologies, and provide examples of how you've successfully collaborated with cross-functional teams.
Showcase Relevant Projects: If you have worked on any significant projects involving data mining, statistical modelling, or machine learning, be sure to include these in your application. Describe your role, the challenges faced, and the outcomes achieved to demonstrate your impact.
Highlight Continuous Learning: Mention any recent courses, certifications, or workshops related to data science, AI, or big data technologies. This shows your commitment to staying updated with industry trends and enhances your profile as a candidate.
How to prepare for a job interview at Synechron
✨Showcase Your Technical Skills
Be prepared to discuss your hands-on experience with programming languages like Python, SAS, and SQL. Highlight specific projects where you've used machine learning libraries such as TensorFlow or PyTorch, and be ready to explain your approach to data analysis and model development.
✨Understand the Business Context
Research the bank's operations and how data science can drive optimisation within their processes. Be ready to discuss how your insights can translate into strategic recommendations that align with their goals.
✨Demonstrate Collaboration Skills
Since the role involves working with cross-functional teams, prepare examples of how you've successfully collaborated with stakeholders in the past. Emphasise your ability to translate complex data needs into actionable solutions.
✨Stay Updated on Industry Trends
Familiarise yourself with the latest trends in AI and machine learning, particularly in the banking sector. Discuss any knowledge you have about Gen AI models and vector databases, as this shows your commitment to staying current in the field.
