At a Glance
- Tasks: Lead AI/ML projects, design analytics solutions, and mentor high-performing teams.
- Company: Join Cognizant Consulting, a global leader in innovative business solutions.
- Benefits: Hybrid work model, competitive salary, and wellness programmes for work-life balance.
- Why this job: Shape the future of business with cutting-edge AI technologies and impactful projects.
- Qualifications: 5+ years in ML engineering, proficiency in Python, and strong communication skills.
- Other info: Exciting opportunities for career growth and innovation in a dynamic environment.
The predicted salary is between 43200 - 72000 ÂŁ per year.
Cognizant Consulting is a global community of experts dedicated to helping clients reimagine their business - blending deep industry and technology advisory capability to create innovative solutions for Fortune 500 clients. We’re looking for our next colleague who’ll join us in shaping the future of business.
As a Lead Data Scientist, AI/ML, you will design, build, and operationalise modern analytics solutions that drive measurable value. You will also take on leadership responsibilities—guiding small, high performing teams as they deliver impactful AI initiatives. You’ll work closely with business stakeholders, product owners, architects, and cross‑functional delivery teams to embed AI/ML into digital products and services, ensuring that complex technical insights translate into clear, compelling business outcomes.
In this role, you will:
- Apply advanced statistical and scientific methods (e.g., hypothesis testing, inference) to frame problems, validate assumptions, and quantify impact.
- Engineer and integrate data across structured and unstructured sources; oversee data wrangling and feature engineering for production grade pipelines.
- Build and guide development of models using Python and libraries such as scikit‑learn, pandas, numpy, and develop deep learning solutions using TensorFlow and PyTorch.
- Process big data at scale using Spark and cloud native tools (e.g., AWS Glue, Azure Data Factory).
- Operationalise ML solutions using MLOps practices—CI/CD for models, reproducible training, and automated deployment.
- Deliver applied ML and AI across predictive analytics, time series forecasting, anomaly detection, NLP, computer vision, and Generative AI (e.g., retrieval systems, chatbots).
- Govern and monitor models in cloud environments; establish retraining schedules, performance monitoring, and risk controls.
- Design machine learning architectures that support pre sales engagements and accelerate the successful initiation of new projects.
- Lead agile delivery practices (Scrum/SAFe) using tools such as JIRA and Trello; ensure backlog health and delivery quality.
- Coach, mentor, and develop team members; advocate for data driven decision making across the organisation.
- Think strategically about data collection, metric design, and ethical AI—driving responsible and transparent use of data.
We believe hybrid work is the way forward as we strive to provide flexibility wherever possible. This is a hybrid position requiring 2-3 days a week in a client or Cognizant office in London, UK. Regardless of your working arrangement, we are here to support a healthy work-life balance through our various wellbeing programmes.
What you must have to be considered:
- 5+ years of hands‑on experience in statistical methods and ML engineering across the end‑to‑end lifecycle (data prep → modelling → deployment → monitoring).
- Proficiency in Python and strong command of ML/DS libraries (scikit‑learn, pandas, numpy, TensorFlow/PyTorch).
- Experience working with GCP, AWS and Azure data services.
- Demonstrated MLOps expertise (CI/CD, model registries, reproducible training, automated deployment).
- Ability to communicate technical insights clearly to non‑technical audiences; strong storytelling with data.
- Proven agile delivery experience; confident in facilitating ceremonies and partnering with product owners.
- Strong grounding in data security and compliance, especially in regulated industries (e.g., BFSI, healthcare, life sciences).
- Working knowledge of cloud‑native software architecture, service design/design thinking, and version control (Git).
- Experience leading small AI teams and mentoring junior data scientists on AI/ML initiatives.
These will help you succeed (nice‑to‑haves):
- Experience designing gen‑AI and agentic AI architectures (e.g., using Google's ADK or similar frameworks).
- Background in real‑time analytics and event‑driven architectures.
- Prior consulting experience (client‑facing, pre‑sales, solutioning) and domain expertise.
- A strong track record of driving innovation and accelerating the adoption of advanced analytics in complex organisations.
We're excited to meet people who share our mission and can make an impact in a variety of ways. Don't hesitate to apply, even if you only meet the minimum requirements listed. Think about your transferable experiences and unique skills that make you stand out as someone who can bring new and exciting things to this role.
Data Scientist Senior Consulting Manager in London employer: Cognizant
Contact Detail:
Cognizant Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist Senior Consulting Manager in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend meetups, and engage in online forums. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving AI/ML. This is your chance to demonstrate your expertise in Python and data engineering—make it shine!
✨Tip Number 3
Prepare for interviews by brushing up on your storytelling skills. Be ready to explain complex technical concepts in simple terms. Remember, it's all about translating your insights into business value!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Data Scientist Senior Consulting Manager in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Lead Data Scientist, AI/ML. Highlight your experience with statistical methods, ML engineering, and any relevant projects that showcase your skills in Python and cloud services.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to tell us why you're passionate about AI/ML and how your background aligns with our mission at Cognizant Consulting. Be sure to mention any leadership experiences you've had.
Showcase Your Projects: If you’ve worked on any cool projects involving data science or AI, don’t hold back! Include links to your GitHub or any relevant portfolios that demonstrate your technical skills and creativity.
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you’re considered for the role you’re excited about!
How to prepare for a job interview at Cognizant
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technical skills listed in the job description, especially Python and ML libraries like scikit-learn and TensorFlow. Prepare to discuss specific projects where you've applied these technologies, as this will show your hands-on experience.
✨Showcase Your Leadership Skills
Since this role involves guiding teams, be ready to share examples of how you've led projects or mentored others. Highlight your experience with agile methodologies and how you've facilitated team success in previous roles.
✨Communicate Clearly
Practice explaining complex technical concepts in simple terms. You’ll need to convey insights to non-technical stakeholders, so think of ways to tell a compelling story with data that demonstrates your impact on business outcomes.
✨Prepare for Scenario Questions
Expect questions that ask how you would handle specific challenges related to AI/ML implementation. Think through potential scenarios involving data wrangling, model deployment, or ethical AI considerations, and be ready to discuss your thought process.