職種概要
Job Description
About the Role
We are seeking a highly skilled Data Scientist to design, develop, and deploy AI/ML models that power data mapping, anomaly detection, and reconciliation automation within large-scale projects. This role combines strong data science expertise with practical engineering skills to build intelligent systems that improve Telecom Platform, reduce manual effort, and ensure high-quality outcomes.
The ideal candidate has hands-on experience with machine learning model development, data engineering, and cloud-based AI/ML workflows, along with exposure to the telecom domain.
Key Responsibilities
AI/ML Model Development
Design, develop, and deploy ML models for automated data mapping, anomaly detection, reconciliation, fraud detection, and churn prediction.
Conduct data profiling, feature engineering, and exploratory analysis to improve accuracy and performance.
Select appropriate algorithms (supervised, unsupervised, reinforcement learning) based on business needs.
Automation & Integration
Build end-to-end ML pipelines for data ingestion, preprocessing, training, validation, and deployment.
Integrate models into Telecom Platform and Automation frameworks, enabling seamless execution in production.
Monitor model performance, implement retraining strategies, and optimize for scalability and reliability.
Collaboration & Delivery
Work with cross-functional teams (engineering, architecture, QA, business SMEs) to align AI solutions with Telecom Platform and enterprise requirements.
Translate business requirements into clear technical specifications, user stories, and acceptance criteria.
Contribute to platform innovation by adopting latest AI/ML advancements in anomaly detection and reconciliation automation.
Knowledge Sharing & Mentorship
Document AI models, frameworks, and best practices for reusability.
Mentor junior engineers/data scientists, fostering a collaborative and learning-oriented environment.
What You Bring
Experience:
5 to 8 years of experience in developing and deploying machine learning models in production.
Hands-on experience in Classification, anomaly detection, or reconciliation automation is highly preferred.
Proven track record of delivering AI/ML projects from ideation to production deployment..
Technical Skills:
Strong knowledge of ML algorithms and techniques (supervised, unsupervised, anomaly detection, NLP, and deep learning).
Proficiency in Python and ML libraries (scikit-learn, TensorFlow, PyTorch).
Experience with data pipelines, ETL/ELT, Delta Lake, and data lakehouse architectures.
Cloud-based ML experience (Azure Data Factory, Azure Databricks, AWS Sagemaker, GCP AI/ML).
Skilled in PySpark for large-scale data processing.
Familiarity with containerization (Docker, Kubernetes) for scalable deployment.
Strong grounding in data reconciliation frameworks and automation techniques.
Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
Building & deploying model using Python Azure Data Factory (ADF), Azure Databricks, PySpark, Delta Lake, ETL/ELT, data pipelines, data lakehouse architecture.
Excellent problem-solving and analytical skills.
Communication and collaboration skills.
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.
Strong understanding of machine learning algorithms and techniques, including supervised, unsupervised, and reinforcement learning.
Excellent problem-solving and analytical skills.
Strong communication and collaboration skills.
必要条件
About the Role
We are seeking a highly skilled Data Scientist to design, develop, and deploy AI/ML models that power data mapping, anomaly detection, and reconciliation automation within large-scale projects. This role combines strong data science expertise with practical engineering skills to build intelligent systems that improve Telecom Platform, reduce manual effort, and ensure high-quality outcomes.
The ideal candidate has hands-on experience with machine learning model development, data engineering, and cloud-based AI/ML workflows, along with exposure to the telecom domain.
Key Responsibilities
AI/ML Model Development
Design, develop, and deploy ML models for automated data mapping, anomaly detection, reconciliation, fraud detection, and churn prediction.
Conduct data profiling, feature engineering, and exploratory analysis to improve accuracy and performance.
Select appropriate algorithms (supervised, unsupervised, reinforcement learning) based on business needs.
Automation & Integration
Build end-to-end ML pipelines for data ingestion, preprocessing, training, validation, and deployment.
Integrate models into Telecom Platform and Automation frameworks, enabling seamless execution in production.
Monitor model performance, implement retraining strategies, and optimize for scalability and reliability.
Collaboration & Delivery
Work with cross-functional teams (engineering, architecture, QA, business SMEs) to align AI solutions with Telecom Platform and enterprise requirements.
Translate business requirements into clear technical specifications, user stories, and acceptance criteria.
Contribute to platform innovation by adopting latest AI/ML advancements in anomaly detection and reconciliation automation.
Knowledge Sharing & Mentorship
Document AI models, frameworks, and best practices for reusability.
Mentor junior engineers/data scientists, fostering a collaborative and learning-oriented environment.
What You Bring
Experience:
5 to 8 years of experience in developing and deploying machine learning models in production.
Hands-on experience in Classification, anomaly detection, or reconciliation automation is highly preferred.
Proven track record of delivering AI/ML projects from ideation to production deployment..
Technical Skills:
Strong knowledge of ML algorithms and techniques (supervised, unsupervised, anomaly detection, NLP, and deep learning).
Proficiency in Python and ML libraries (scikit-learn, TensorFlow, PyTorch).
Experience with data pipelines, ETL/ELT, Delta Lake, and data lakehouse architectures.
Cloud-based ML experience (Azure Data Factory, Azure Databricks, AWS Sagemaker, GCP AI/ML).
Skilled in PySpark for large-scale data processing.
Familiarity with containerization (Docker, Kubernetes) for scalable deployment.
Strong grounding in data reconciliation frameworks and automation techniques.
Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
Building & deploying model using Python Azure Data Factory (ADF), Azure Databricks, PySpark, Delta Lake, ETL/ELT, data pipelines, data lakehouse architecture.
Excellent problem-solving and analytical skills.
Communication and collaboration skills.
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.
Strong understanding of machine learning algorithms and techniques, including supervised, unsupervised, and reinforcement learning.
Excellent problem-solving and analytical skills.
Strong communication and collaboration skills.
Soft Skills:
Strong problem-solving and critical thinking ability.
Ability to simplify complex AI/ML concepts for both technical and business stakeholders.
Comfortable working in fast-paced Agile environments with shifting priorities.
Collaborative, proactive, and outcome-oriented mindset.
Qualifications:
Bachelor’s degree in Computer Science or related field from IIT.
Master’s degree or equivalent advanced degree preferred.
Proven track record of delivering data science projects from ideation to production.
Strong communication skills and the ability to tell compelling stories with data.
Comfortable with both structured and unstructured data sets.
Certifications in AI/ML, cloud platforms, or data science frameworks are a plus.
職務内容
About the Role
We are seeking a highly skilled Data Scientist to design, develop, and deploy AI/ML models that power data mapping, anomaly detection, and reconciliation automation within large-scale projects. This role combines strong data science expertise with practical engineering skills to build intelligent systems that improve Telecom Platform, reduce manual effort, and ensure high-quality outcomes.
The ideal candidate has hands-on experience with machine learning model development, data engineering, and cloud-based AI/ML workflows, along with exposure to the telecom domain.
Key Responsibilities
AI/ML Model Development
Design, develop, and deploy ML models for automated data mapping, anomaly detection, reconciliation, fraud detection, and churn prediction.
Conduct data profiling, feature engineering, and exploratory analysis to improve accuracy and performance.
Select appropriate algorithms (supervised, unsupervised, reinforcement learning) based on business needs.
Automation & Integration
Build end-to-end ML pipelines for data ingestion, preprocessing, training, validation, and deployment.
Integrate models into Telecom Platform and Automation frameworks, enabling seamless execution in production.
Monitor model performance, implement retraining strategies, and optimize for scalability and reliability.
Collaboration & Delivery
Work with cross-functional teams (engineering, architecture, QA, business SMEs) to align AI solutions with Telecom Platform and enterprise requirements.
Translate business requirements into clear technical specifications, user stories, and acceptance criteria.
Contribute to platform innovation by adopting latest AI/ML advancements in anomaly detection and reconciliation automation.
Knowledge Sharing & Mentorship
Document AI models, frameworks, and best practices for reusability.
Mentor junior engineers/data scientists, fostering a collaborative and learning-oriented environment.
What You Bring
Experience:
3 to 5 years of experience in developing and deploying machine learning models in production.
Hands-on experience in Classification, anomaly detection, or reconciliation automation is highly preferred.
Proven track record of delivering AI/ML projects from ideation to production deployment..
Technical Skills:
Strong knowledge of ML algorithms and techniques (supervised, unsupervised, anomaly detection, NLP, and deep learning).
Proficiency in Python and ML libraries (scikit-learn, TensorFlow, PyTorch).
Experience with data pipelines, ETL/ELT, Delta Lake, and data lakehouse architectures.
Cloud-based ML experience (Azure Data Factory, Azure Databricks, AWS Sagemaker, GCP AI/ML).
Skilled in PySpark for large-scale data processing.
Familiarity with containerization (Docker, Kubernetes) for scalable deployment.
Strong grounding in data reconciliation frameworks and automation techniques.
Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
Building & deploying model using Python Azure Data Factory (ADF), Azure Databricks, PySpark, Delta Lake, ETL/ELT, data pipelines, data lakehouse architecture.
Excellent problem-solving and analytical skills.
Communication and collaboration skills.
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.
Strong understanding of machine learning algorithms and techniques, including supervised, unsupervised, and reinforcement learning.
Excellent problem-solving and analytical skills.
Strong communication and collaboration skills.
Soft Skills:
Strong problem-solving and critical thinking ability.
Ability to simplify complex AI/ML concepts for both technical and business stakeholders.
Comfortable working in fast-paced Agile environments with shifting priorities.
Collaborative, proactive, and outcome-oriented mindset.
Qualifications:
Bachelor’s degree in Computer Science or related field.
Master’s degree or equivalent advanced degree preferred.
Proven track record of delivering data science projects from ideation to production.
Strong communication skills and the ability to tell compelling stories with data.
Comfortable with both structured and unstructured data sets.
Certifications in AI/ML, cloud platforms, or data science frameworks are a plus.
私たちが提供するもの
Exciting Projects: We focus on industries like High-Tech, communication, media, healthcare, retail and telecom. Our customer list is full of fantastic global brands and leaders who love what we build for them.
Collaborative Environment: You Can expand your skills by collaborating with a diverse team of highly talented people in an open, laidback environment — or even abroad in one of our global centers or client facilities!
Work-Life Balance: GlobalLogic prioritizes work-life balance, which is why we offer flexible work schedules, opportunities to work from home, and paid time off and holidays.
Professional Development: Our dedicated Learning & Development team regularly organizes Communication skills training(GL Vantage, Toast Master),Stress Management program, professional certifications, and technical and soft skill trainings.
Excellent Benefits: We provide our employees with competitive salaries, family medical insurance, Group Term Life Insurance, Group Personal Accident Insurance , NPS(National Pension Scheme ), Periodic health awareness program, extended maternity leave, annual performance bonuses, and referral bonuses.
Fun Perks: We want you to love where you work, which is why we host sports events, cultural activities, offer food on subsidies rates, Corporate parties. Our vibrant offices also include dedicated GL Zones, rooftop decks and GL Club where you can drink coffee or tea with your colleagues over a game of table and offer discounts for popular stores and restaurants!
GlobalLogicについて
GlobalLogic, a Hitachi Group Company, is a trusted digital engineering partner to the world’s largest and most forward-thinking companies. Since 2000, we’ve been at the forefront of the digital revolution – helping create some of the most innovative and widely used digital products and experiences. Today we continue to collaborate with clients in transforming businesses and redefining industries through intelligent products, platforms, and services.
