Senior Business Analyst Data Analytics & AI Remote

TE Connectivity
Tadley
2 months ago
Applications closed

Related Jobs

View all jobs

Power BI Data Analyst

Senior Data Analyst

Senior Data Governance Analyst

Actuarial Data Scientist

Actuarial Data Scientist

Data Analyst

Senior Business Analyst Data Analytics & AI Remote

Join to apply for the Senior Business Analyst Data Analytics & AI Remote role at TE Connectivity.


Location: Little London, England, United Kingdom.


Seniority: Mid-Senior level | Employment Type: Full-time | Job Function: Research, Analyst, and Information Technology | Industries: Appliances, Electrical, and Electronics Manufacturing.


Why you should join

We are seeking a strategic and hands‑on Business Analyst to lead data, analytics, and AI initiatives within the Energy Business Unit. This role will drive data quality, advanced analytics, and AI adoption to enable business transformation, accelerate enterprise standards, and modernize legacy systems. You will act as a bridge between business opportunity, digital capability, and advanced analytics, ensuring every initiative delivers tangible value.


Key Responsibilities

  • AI Strategy & Product Delivery

    • Lead end‑to‑end design and delivery of AI‑powered tools and automation solutions across business functions.
    • Identify and prioritize high‑value AI, advanced analytics, and data science use cases aligned with strategic priorities.
    • Build ROI‑based business cases and own success metrics from prototype through scale.
    • Assess the suitability and potential impact of emerging technologies, including agentic AI, LLMs, and cognitive automation.


  • Business Engagement & Demand Shaping

    • Partner with business leaders to identify analytics, data science, and AI opportunities.
    • Shape and prioritize data‑driven use cases aligned with business goals and enterprise solutions.
    • Establish a demand‑management framework to connect business needs with data capabilities.


  • Data Platform Adoption & Modernization

    • Drive adoption of scalable data products and the One Data Platform.
    • Advise on data integration, self‑service analytics, and advanced AI/ML capabilities.
    • Lead retirement of legacy platforms and migration to modern solutions.
    • Define KPIs for system decommissioning and cost optimization.


  • Data & AI Literacy, Enablement, and Culture Building

    • Execute training programs and “AI Quick Start” initiatives to enable self‑service analytics and AI adoption.
    • Foster a data‑driven culture through awareness, best practices, and success stories.
    • Build and sustain an AI community of practice to share tools and lessons learned.


  • Data Readiness & Governance

    • Partner with Data & Analytics teams to ensure priority AI use cases have required data access and quality.
    • Identify and resolve critical data gaps, recommend improvements, and ensure alignment with governance and security standards.
    • Leverage enterprise data platforms (Salesforce, SAP, Databricks, etc.) for active AI projects.


  • Responsible AI & Measurement

    • Establish and communicate clear principles for responsible AI use, ensuring ethical, transparent, and compliant application.
    • Develop KPIs and dashboards to track AI adoption, impact, and ROI.
    • Measure business value delivered through AI (revenue growth, cost avoidance, efficiency gains).


  • Continuous Innovation & Partnerships

    • Stay current on emerging AI tools and technologies.
    • Evaluate partnerships with vendors, startups, and enterprise teams to accelerate deployment.
    • Encourage experimentation through safe pilot environments and scale successful ideas.



Required Qualifications

  • Bachelor’s degree in Computer Science, Engineering, Data Science, or related field; advanced degree (MBA, MS, or equivalent) preferred.
  • 10+ years in data and analytics leadership, digital product management, or applied AI roles within complex organizations.
  • Proven track record delivering measurable business outcomes through AI, automation, or digital transformation.
  • Hands‑on experience with LLMs, prompt engineering, Python scripting, or workflow automation.
  • Strong understanding of data systems (Databricks, Snowflake, Azure Synapse, SAP, Salesforce) and governance principles.
  • Exceptional ability to translate complex technical concepts into business‑relevant narratives and use cases.
  • Strong communication and influence skills; able to inspire adoption and align senior stakeholders.
  • Demonstrated success in driving organizational change and data literacy.

Preferred Qualifications

  • Experience in manufacturing, supply chain, sales & ops planning, or shop floor data.
  • Knowledge of modern cloud architectures (Databricks, Snowflake, Azure, AWS, GCP).
  • Strong understanding of AI ethics, data privacy, and regulatory frameworks.
  • Effective collaboration across technical and business teams.

Core Competencies

  • Values: Integrity, Accountability, Inclusion, Innovation, Teamwork.
  • Skills: Business acumen, change management, strategic thinking, leadership, collaboration.

Success Metrics

  • Business impact: Value created via AI‑enabled use cases (revenue growth, cost savings, efficiency).
  • Adoption: Percentage of teams using AI tools and automation.
  • Enablement: Number of employees completing AI literacy programs.
  • Scalability: Reuse rate of AI models and tools across functions.
  • Cultural change: Demonstrated AI‑first mindset and proactive experimentation.

Join our team and play a pivotal role in transforming how the Energy Business Unit leverages data, analytics, and AI to drive growth, efficiency, and innovation. If you’re passionate about building innovative solutions and shaping the future of work, we’d love to hear from you.


#J-18808-Ljbffr

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.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.