Senior Lead Data Scientist at Siemens in Gurugram, Haryana, India | Kablio

Role

Description

python coding

feature engineering

data pipelines

predictive modeling

statistical analysis

a/b testing

Write clean, efficient code in Python or R for modeling and analysis logic.
Identify trends, correlations, and actionable insights to support business decisions.
Perform feature engineering, feature selection, and model optimization.
Stay updated with emerging trends in ML, AI, data analytics, and tools.
Build and maintain data preprocessing and transformation pipelines.
Collect, explore, and analyze large datasets using statistical methods.
Develop, train, and validate predictive models for classification, regression, clustering, time-series forecasting, or recommendation systems.
Contribute to improving internal data science frameworks and practices.
Translate complex analytical results into actionable recommendations.
Support product development through data-driven insights and modeling.
Apply statistical methods to validate hypotheses and measure impact.
Work with data engineers to ensure reliable data availability and quality.
Ensure the integrity and rigor of analytical methodologies.
Design and run A/B tests or experimental studies.
Experiment with new algorithms, technologies, and approaches.
Work closely with product managers, engineers, domain experts, and leadership teams.
Communicate findings through clear visualizations, dashboards, and reports.

Requirements

data science

5+ years

spark

tensorflow

python

tableau

Bachelor’s or master’s degree in data science, Computer Science, Statistics, Mathematics, Engineering, or related field.
Collaborative mindset with strong ownership and accountability.
Experience with big data tools (Spark, Databricks, Kafka, Hadoop).
Proficiency in ML frameworks (Scikit-learn, TensorFlow, PyTorch).
5-8+ years of experience in data science or applied analytics.
Predictive Modeling & Machine Learning
Employ ML frameworks such as Scikit-learn, TensorFlow, PyTorch, or XGBoost.
Familiarity with MLOps platforms (MLflow, Kubeflow, DVC).
Excellent communication and storytelling skills.
Knowledge of data visualization tools (Tableau, Power BI, Plotly).
Good understanding of statistical modeling, hypothesis testing, and experimental design.
Experience in domain-specific analytics (finance, IoT, geospatial, utility networks, etc.).
Experience working with SQL and cloud data platforms (AWS, Azure, GCP).
Strong analytical and problem-solving abilities.
Strong experience with Python or R and data libraries (Pandas, NumPy, SciPy).
Background in time-series forecasting, NLP, or computer vision.
Ability to explain technical results to non-technical stakeholders.

Benefits

Information not given or found

Training + Development

Information not given or found

Interview process

Information not given or found

Visa Sponsorship

Information not given or found

Security clearance

Information not given or found

Company

Overview

Founded in 1847

Historical Milestone

Established as a small telegraph company and transformed into a global leader in technology.

The company drives progress with solutions in electrification, automation, and digitalization across industries.
With cutting-edge products and services, it leads sustainable innovation for industries like energy, manufacturing, and infrastructure.
Its impressive portfolio includes smart grids, automated factories, advanced healthcare technologies, and solutions for urban mobility.
Siemens’ digital solutions enhance efficiency and sustainability in everything from smart buildings to electric vehicles.
It has contributed to iconic projects, including energy-efficient train systems and automation in key industrial sectors.
Transforming how industries use digital technology to optimize operations and tackle global challenges.

Culture + Values

Leading change is a team effort
Our people are enthusiasts with diverse backgrounds, skills and interests
Every individual is valued
Ownership – empowering employees to take responsibility
Collaboration – leveraging unique strengths to find creative solutions
Respect & Inclusion – diverse, respectful, flat hierarchies, flexibility, celebrating individual contributions

Environment + Sustainability

90% reduction

Scope 1 & 2 emissions by 2030

Achieve a 90% reduction in own-operations greenhouse gas emissions compared to 2019 levels by 2030.

144 million tonnes

CO₂-equivalent avoided in FY 2024

Products sold in FY 2024 helped customers avoid approximately 144 million tonnes of CO₂-equivalent emissions.

100% renewable

Electricity by 2030

Transition to 100% renewable electricity across operations by 2030, aligned with the RE100 initiative.

90% reduction

Scope 3 emissions by 2050

Achieve a 90% reduction in value-chain (Scope 3) emissions by 2050, validated by the Science-Based Targets initiative.

Achieve net‑zero greenhouse‑gas emissions across the value chain by 2050
Own‑operations net‑zero by 2030
Value‑chain (Scope 3) target: 30% reduction by 2030
€650 million invested in decarbonization efforts by 2030
100% EV fleet (EV100) by 2030
100% net‑zero buildings (EP100) by 2030
Scope 1 & 2 emissions reduced by 50% since 2019, Scope 3 by 15% since 2019
Products sold in FY 2024 helped customers avoid ~144 million tonnes CO₂‑equivalent
Eco‑design standard achievement: 54%; 25,000 Siemens products labeled “EcoTech” for sustainable design
Landfill waste down ~12%; recycling share of total waste 84%

Inclusion & Diversity

Women at 32%

Top Management Increase

Women in top management roles increased significantly from a baseline of 23% in FY 2020 to 32% in FY 2024.

DEGREE strategic framework includes 'Equity' as a measurable field of action
Global Equity program focuses on equal rights in hiring and internal promotion processes
SBTi‑validated targets exclude DEI but inclusion embedded in governance and career development

Articles

Siemens Jobs & Careers – US

Siemens Student & Early Career Programs

Siemens Benefits (Smart Buildings)

Siemens Academic Blog – FIRST Robotics

Role

Description

python coding

feature engineering

data pipelines

predictive modeling

statistical analysis

a/b testing

Write clean, efficient code in Python or R for modeling and analysis logic.
Identify trends, correlations, and actionable insights to support business decisions.
Perform feature engineering, feature selection, and model optimization.
Stay updated with emerging trends in ML, AI, data analytics, and tools.
Build and maintain data preprocessing and transformation pipelines.
Collect, explore, and analyze large datasets using statistical methods.
Develop, train, and validate predictive models for classification, regression, clustering, time-series forecasting, or recommendation systems.
Contribute to improving internal data science frameworks and practices.
Translate complex analytical results into actionable recommendations.
Support product development through data-driven insights and modeling.
Apply statistical methods to validate hypotheses and measure impact.
Work with data engineers to ensure reliable data availability and quality.
Ensure the integrity and rigor of analytical methodologies.
Design and run A/B tests or experimental studies.
Experiment with new algorithms, technologies, and approaches.
Work closely with product managers, engineers, domain experts, and leadership teams.
Communicate findings through clear visualizations, dashboards, and reports.

Requirements

data science

5+ years

spark

tensorflow

python

tableau

Bachelor’s or master’s degree in data science, Computer Science, Statistics, Mathematics, Engineering, or related field.
Collaborative mindset with strong ownership and accountability.
Experience with big data tools (Spark, Databricks, Kafka, Hadoop).
Proficiency in ML frameworks (Scikit-learn, TensorFlow, PyTorch).
5-8+ years of experience in data science or applied analytics.
Predictive Modeling & Machine Learning
Employ ML frameworks such as Scikit-learn, TensorFlow, PyTorch, or XGBoost.
Familiarity with MLOps platforms (MLflow, Kubeflow, DVC).
Excellent communication and storytelling skills.
Knowledge of data visualization tools (Tableau, Power BI, Plotly).
Good understanding of statistical modeling, hypothesis testing, and experimental design.
Experience in domain-specific analytics (finance, IoT, geospatial, utility networks, etc.).
Experience working with SQL and cloud data platforms (AWS, Azure, GCP).
Strong analytical and problem-solving abilities.
Strong experience with Python or R and data libraries (Pandas, NumPy, SciPy).
Background in time-series forecasting, NLP, or computer vision.
Ability to explain technical results to non-technical stakeholders.

Benefits

Information not given or found

Training + Development

Information not given or found

Interview process

Information not given or found

Visa Sponsorship

Information not given or found

Security clearance

Information not given or found

Company

Overview

Founded in 1847

Historical Milestone

Established as a small telegraph company and transformed into a global leader in technology.

The company drives progress with solutions in electrification, automation, and digitalization across industries.
With cutting-edge products and services, it leads sustainable innovation for industries like energy, manufacturing, and infrastructure.
Its impressive portfolio includes smart grids, automated factories, advanced healthcare technologies, and solutions for urban mobility.
Siemens’ digital solutions enhance efficiency and sustainability in everything from smart buildings to electric vehicles.
It has contributed to iconic projects, including energy-efficient train systems and automation in key industrial sectors.
Transforming how industries use digital technology to optimize operations and tackle global challenges.

Culture + Values

Leading change is a team effort
Our people are enthusiasts with diverse backgrounds, skills and interests
Every individual is valued
Ownership – empowering employees to take responsibility
Collaboration – leveraging unique strengths to find creative solutions
Respect & Inclusion – diverse, respectful, flat hierarchies, flexibility, celebrating individual contributions

Environment + Sustainability

90% reduction

Scope 1 & 2 emissions by 2030

Achieve a 90% reduction in own-operations greenhouse gas emissions compared to 2019 levels by 2030.

144 million tonnes

CO₂-equivalent avoided in FY 2024

Products sold in FY 2024 helped customers avoid approximately 144 million tonnes of CO₂-equivalent emissions.

100% renewable

Electricity by 2030

Transition to 100% renewable electricity across operations by 2030, aligned with the RE100 initiative.

90% reduction

Scope 3 emissions by 2050

Achieve a 90% reduction in value-chain (Scope 3) emissions by 2050, validated by the Science-Based Targets initiative.

Achieve net‑zero greenhouse‑gas emissions across the value chain by 2050
Own‑operations net‑zero by 2030
Value‑chain (Scope 3) target: 30% reduction by 2030
€650 million invested in decarbonization efforts by 2030
100% EV fleet (EV100) by 2030
100% net‑zero buildings (EP100) by 2030
Scope 1 & 2 emissions reduced by 50% since 2019, Scope 3 by 15% since 2019
Products sold in FY 2024 helped customers avoid ~144 million tonnes CO₂‑equivalent
Eco‑design standard achievement: 54%; 25,000 Siemens products labeled “EcoTech” for sustainable design
Landfill waste down ~12%; recycling share of total waste 84%

Inclusion & Diversity

Women at 32%

Top Management Increase

Women in top management roles increased significantly from a baseline of 23% in FY 2020 to 32% in FY 2024.

DEGREE strategic framework includes 'Equity' as a measurable field of action
Global Equity program focuses on equal rights in hiring and internal promotion processes
SBTi‑validated targets exclude DEI but inclusion embedded in governance and career development

Articles

Siemens Jobs & Careers – US

Siemens Student & Early Career Programs

Siemens Benefits (Smart Buildings)

Siemens Academic Blog – FIRST Robotics