Connecting Odds
Philips

Lead Data Scientist

Philips
BangalorePosted Discovered via the company's careers siteMatch locked
Full time7+ years exp

Job Title

Lead Data Scientist

Lead Data Scientist

Your role: The

Machine Learning

and

Generative AI

systems - owning the

full lifecycle

from

model development

to

scalable cloud deployment

and ongoing

performance monitoring

. In addition, the role partners with

commercial stakeholders

translate market/customer data into

decision-ready insights

and

AI-enabled analytics

solutions that drive

measurable outcomes

Operating with a

builder and translator mindset

, the individual rapidly develops

MVP analytics solutions

, leverages

AI

to accelerate

insight generation

, and ensures strong product engineering fundamentals,

data quality

, and

governance

. The role plays a critical part in establishing a

single source of truth

for

performance management

across markets and channels while elevating analytics maturity from descriptive reporting to

predictive

and

insight-led decision making

. Key Responsibilities 1) ML & Deep Learning Model Development •

Design

,

train

, and

ML models

for prediction, classification, ranking,

time-series forecasting

,

anomaly detection

,

NLP

, and

recommendation

use cases.

  • Build robust

experimentation workflows

(

train/validation strategy

, ablations,

error analysis

) and improve

model quality

through iterative tuning.

  • Ensure

reproducibility

and

maintainability

through clean code practices,

versioning

, and

automated testing

.

  1. GenAI Engineering (LLMs, RAG / MCP / fine-tuning, Agents)
  • Build enterprise-grade

LLM applications

using

RAG

(retrieval-augmented generation),

MCP

, and

fine-tuning

approaches:

chunking

strategies,

embedding generation

,

hybrid retrieval

,

reranking

,

prompt templates

, and

citation/attribution

patterns.

  • Develop

LLM applications

with

tool use

/

function calling

patterns and

agentic workflows

where appropriate.

  • Implement

systematic evaluation

: curated

eval sets

,

prompt regression tests

,

hallucination checks

,

retrieval quality metrics

, and automated

quality gates

.

  1. ML & LLM Operations: Productionization, Deployment & Monitoring
  • Deploy and operate

real-time

and

batch inference

solutions on

Azure

using

managed endpoints

and/or

containerized serving

.

  • Build

CI/CD

for ML systems:

automated packaging

,

container builds

,

model validation tests

,

staged rollouts

, and

rollback strategies

.

  • Establish

lifecycle management

:

model registry

/versioning,

lineage

,

promotion workflows

, and

release governance

.

  • Implement

observability

:

latency

,

throughput

,

cost

,

drift

signals,

data quality checks

,

alerts

, and

performance degradation

monitoring.

  1. Pipeline Orchestration & Automation (Train → Deploy)
  • Build

standardized ML pipelines

for

training

,

evaluation

, and

deployment

using

orchestration tools

(cloud-native pipelines and/or platform tools).

  • Automate

dataset/version management

,

feature generation

,

scheduled retraining

triggers, and

approval workflows

. •

Define repeatable patterns for scalable experimentation and reliable production delivery .

  1. Analytics Products, Dashboards & Data Governance
  • Own key

analytics outputs

as

products

(

dashboards

,

reusable datasets

,

internal tools

), continuously improving them based on usage patterns and

performance gaps

.

  • Build and automate

dashboards

and analytical components using scalable

SQL

logic,

Python

transformations, and

reusable modules

.

  • Act as owner for critical

commercial/syndicated datasets

(e.g., GfK, Circana, Nielsen or equivalent):

definitions

,

assumptions

, and

limitations

, ensuring

transparent logic

and

trust

in outputs.

  • Partner with

data engineering/IT

to ensure

data quality

,

harmonization

, and

governance

through strong

validation

and

reconciliation

practices.

  1. Stakeholder Partnership & Decision Support (Lightweight, High Impact)
  • Serve as

trusted analytics thought partner

to

senior stakeholders

(e.g., BU leadership, Sales, Marketing, Finance), shaping

problem statements

and aligning on

success metrics

.

  • Translate complex analytics into clear

recommendations

with a

decision-oriented storyline

("

so-what / now-what

"), tailored for

leadership forums

and

reviews

.

  • Support

performance reviews

,

planning cycles

, and high-priority

ad-hoc requests

with

speed

,

rigor

, and

confidence

; proactively

challenge assumptions

with

fact-based insights

.

  1. Responsible AI, Security, and Risk Controls (GenAI-ready)
  • Implement

guardrails

:

prompt injection

defenses,

sensitive data

protections,

output validation

, and

secure tool execution

patterns.

  • Apply

responsible AI

practices: transparent

evaluation criteria

,

auditability

, and

risk controls

aligned to enterprise needs.

  1. Technical Leadership (Lead-level Expectations)
  • Set

engineering standards

for DS/ML codebases:

design docs

,

code review

practices,

testing discipline

, and

production readiness

checklists. •

Mentor

data scientists/ML engineers on

modeling

,

GenAI engineering

, and

MLOps

best practices.

  • Lead

architectural decisions

across modeling approaches,

retrieval stack

,

serving patterns

, and

evaluation strategy

. Core Skills & Competencies

Must-have (Technical)

  • Strong

Python

(

production-quality

coding) and solid CS fundamentals; strong

SQL

for

data access

and

validation

.

  • Depth in

ML

: Traditional

ML

exposure and at least one

deep learning

framework (

PyTorch/TensorFlow

), with strong understanding of

metrics

and

failure modes

. •

GenAI implementation

:

RAG / MCP / fine-tuning

,

embeddings/vector search

,

prompt orchestration

,

evaluation harnesses

, and

LLM application patterns

. •

Production deployment

experience on

AWS

or

Azure

(

model/LLM app deployment

,

API serving

,

scaling

,

monitoring

). •

MLOps

tooling:

experiment tracking

,

model registry

,

CI/CD

, and

pipeline orchestration

(e.g.,

MLflow

or equivalent patterns).

Good-to-have (Business + Influence)

  • Strong

business acumen

and ability to connect disparate

data points

into

compelling narratives

that influence

senior stakeholders

. •

Builder/MVP mindset

-

rapid prototyping

and iterating based on

stakeholder feedback

while maintaining

data quality

and

governance

Education Requirements •

Bachelor’s degree

in

engineering

,

Computer Science

,

Statistics

,

Economics

,

Mathematics

, or a related

quantitative

field. •

Master’s degree

preferred (e.g.,

Data Analytics

,

Business Analytics

,

Applied Statistics

,

Economics

,

AI

, or

MBA

with strong analytics focus). •

Continuous learning

mindset expected, with demonstrated

upskilling

in

advanced analytics

,

AI

, or

data engineering

concepts (formal or informal).

You're the right fit if

  • Proven track record of owning

end-to-end analytics domains

, not just contributing to

isolated analyses

or consuming

pre-built reports

. •

7–12+ years

in hands-on

Data Science / ML Engineering

with multiple

production deployments

owned

end-to-end

.

  • Demonstrated ability to take solutions from

experimentation → production

(

reproducible pipelines

, deployment to

managed endpoints

/container platforms,

monitoring

+

iterative improvement

).

  • Strong

GenAI delivery

record: shipped

LLM applications

with measurable

quality controls

,

safety measures

, and

operational readiness

.

  • Experience operating in

complex, matrixed environments

and partnering with

senior stakeholders

to drive

insight-led decision making

  • Hands-on exposure to

AI-enabled analytics

, including the use of

GenAI tools

(e.g.,

ChatGPT

,

Claude

, or similar) to accelerate

insight generation

, analysis, or

productivity

.

  • Strong experience partnering with

senior business stakeholders

(BU leaders, Sales, Marketing, Finance),

influencing decisions

through

insight-led storytelling

. #Personalhealth How we work together We believe that we are better together than apart. For our office-based teams, this means working in-person at least 3 days per week. Onsite roles require full-time presence in the company's facilities. Field roles are most effectively done outside of the company's main facilities, generally at the customers' or suppliers' locations.

this role is an office

role.

About Philips

We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others.

  • Learn more about our business .
  • Discover our rich and exciting history .
  • Learn more about our purpose .

If you're interested in this role and have many, but not all, of the experiences needed, we encourage you to apply. You may still be the right candidate for this or other opportunities at Philips. Learn more about our culture of impact with care here .

Not included in the source posting: what you'll do, qualifications, benefits.

Skills

machine-learningartificial-intelligencegenerative-aillmragazureci-cddeep-learningmlopspythonsqlaws

Who can apply

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