Technical Senior Business Analyst

IBU Consulting Pvt Ltd
London
1 year ago
Applications closed

Related Jobs

View all jobs

Copy of Graduate Data Science Consultant

Senior Data Engineer London ·

Business and Data Analyst Senior Associate

Business and Data Analyst Senior Associate

Senior Data Analyst

Senior Data Analyst

Full job description that includes: Mandatory skills: SQL, Cucumber Gherkin, ALM, MS Office, Python 3.0, Jira, Jama, Confluence, Sharepoint, Microstrategy, SAP Business Object, DQIM Domain Credit Risk Management (understanding of Regulatory Capital Adequacy under Basel 2, 3, Fed Reserve SR letters, CRD, BCBS239 covering ACS and Internal Stress Testing, Securitisations and OTCs exposures NMR, Market and CCR treated under SACCR, VaR, sVaR, RWA, CVA, FRTB, Wholesale metrics, VaR models, Risk Engine, Trade workflow, mapping, CCR, MR Regulatory reporting, PnL) Nice to have skills: Microstrategy, Python, Databricks Responsibilities Gather business requirements, determine data and reporting insights and findings, build datasets and support Tableau Reports/Dashboards development Develop multiple complex SQL queries and views (pulling from complex, multiple data sources, synthesizing and formatting to prepare for distribution to stakeholders) Support end-to-end Business Intelligence lifecycle development processes as needed, which includes, but is not limited to, capturing business requirement definitions (BRD/user stories), creating functional requirement definitions (FRD), technical design documents (TDD), and running user acceptance testing (UAT) Power business strategy and execution by building clever system integrations Maintain reliable data pipelines and architect scalable data solutions Collaborate closely with Finance and IT to establish optimized, source-of-truth datasets for cross-functional reporting Requirements: Bachelor's Degree (Technology related field) or equivalent work experience. 12 relevant years' experience in assembling, querying large, complex sets of data and building datasets for visualizations that meet business requirements Design and develop interactive Tableau Dashboards and Reports providing actionable insights or any other visualization tools Excellent coding skills in SQL with experience in creating curated datasets from different data sources Collaborate with Data Engineers to integrate various data sources into Tableau ensuring data accuracy and data integrity Provide a clear path to the automation of metrics and data to the business stakeholders with a clear articulation of dependencies and asks Strong understanding of applied machine learning topics Ability to explain complex, technical subjects to non-technical audiences Consistent track record of leading complex data projects Values documentation, collaboration, and mentoring

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.