Matching exceptional talent with companies that value substance over stories.

A.P.

Data Science Research Engineer

Seasoned data scientist building production ML systems in fintech, specialising in conversational AI and generative systems with strong business impact focus

View CV

🧠 How they think

• We found her approach to be remarkably systematic and business-oriented. Throughout the interview, she consistently connected technical decisions back to business metrics and user outcomes.

• When discussing the chatbot project, she demonstrated structured thinking by breaking down the problem into clear phases - from stakeholder alignment to model evaluation to deployment. She thinks in terms of prioritisation rather than trying to solve everything at once, showing maturity in understanding business constraints.

• Her approach felt methodical yet flexible, always considering both technical feasibility and business impact when making decisions.

⚡️ Fast facts

EDUCATION

Masters in Data Science from King's College London; Undergrad in Computer Science from BITS Pilani

INDUSTRY EXPERTISE

Fintech, Banking, Artificial Intelligence, Machine Learning, Data Analysis, Financial Services, Customer service, Payments

YEARS OF EXPERIENCE

5.5+

EXPERIENCE WITH TOOLS

Python, SQL, RASA, TensorFlow, PyTorch, Keras, Scikit-Learn, HuggingFace, LangChain, AWS, BERT, LLMs, NLP, FastAPI, Git, Metabase

NOTICE PERIOD

30 days

PRIORITIES FOR NEXT ROLE

Career growth, Impactful work, Competitive compensation

HEAR THEM IN ACTION

00:00 / 01:04

💪 What drives them

• She's genuinely fascinated by making AI more accessible to broader audiences. When she spoke about her medical friends using ChatGPT, there was real excitement in her voice about democratising these technologies.

• She seems driven by the intersection of technical innovation and practical impact rather than pure research. Her passion extends beyond just building models to creating systems that genuinely improve user experiences.

• Outside work, her love for mountains and plants suggests someone who values growth, nurturing, and exploration - themes that clearly carry into her professional approach to learning and development.

😇 Understanding of self

• She showed genuine self-awareness about both her strengths and development areas.

• When discussing the data selection challenges, she openly acknowledged that technical skills alone weren't sufficient and that she had to develop her soft skills and stakeholder management abilities.

• She recognised that her early career approach of wanting to 'build everything' needed to evolve toward more strategic prioritisation.

• Her awareness of trial and error limitations and desire to learn more structured approaches suggested someone who actively reflects on their working methods and seeks improvement.

📣 Communication style

She explained the complex chatbot architecture and evaluation challenges with remarkable clarity, breaking down technical concepts into logical components.

When describing the business impact, she naturally wove together technical details with business context, making it easy to follow her reasoning.

She used specific examples effectively - like the medical friends using ChatGPT - to illustrate broader points about AI accessibility. Her communication felt structured but not overly formal, and she showed good awareness of her audience by explaining technical concepts without assuming deep ML knowledge.

⚠️ Potential risks

• She seemed very focused on the technical execution side, which whilst impressive, occasionally made us wonder about her commercial instincts when not given explicit business direction.

• Her passion for cutting-edge AI might sometimes conflict with practical business needs in more conservative environments.

• We also noticed she mentioned trial and error approaches quite frequently, which could indicate a need for more structured experimentation frameworks in some contexts.

• Her experience has been primarily in fast-moving startup environments, so adapting to larger, more bureaucratic organisations might require adjustment.

🧑‍🎨 Life beyond work

• She revealed a wonderfully creative side through her 'movie trailer' response, painting a vivid picture of mountains, plants, books, and dogs that immediately conveyed her personality. Her passion for houseplants particularly stood out - she spoke about them with genuine affection, describing how they respond to nurturing and care.

• Her love for mountain trekking suggests someone who enjoys challenges and exploration beyond her professional life.

🥵 Toughest problem solved

The conversational AI chatbot project revealed her ability to navigate complex technical and organisational challenges. What impressed us most was how she approached the data selection problem - when stakeholders had conflicting views on training data, she didn't just default to technical solutions but used data and metrics to build consensus. She demonstrated real problem-solving maturity by recognising that 90% of the work was data processing rather than fancy modelling.

The project's success - reducing containment rates from 40% to 10% - shows she focuses on meaningful business outcomes rather than just technical achievements.