\n \n Data Scientist\n \n
\n\n
\n
\n \n Location: Hyderabad (Hybrid)\n \n
\n\n
\n
\n \n Experience: 6–8 years\n \n
\n\n
\n
\n \n Role Overview\n \n
\n\n We are seeking a Data Scientist with a strong engineering background to design, build, train, and operationalize machine learning models that deliver measurable business impact.\n
\n\n You will work end-to-end across feature engineering, model training, inference, and post-processing, leveraging a modern, cloud-native ML platform built on GCP and Kubernetes.\n
\n\n This role blends strong statistical and machine learning expertise with hands-on MLOps practices, ensuring models are reliable, scalable, and production-ready.\n
\n\n
\n
\n \n Key Responsibilities\n \n
\n\n \n Model Development & Data Science\n \n
\n\n ? Develop, train, and validate machine learning models using Python.\n
\n\n ? Perform feature engineering, exploratory data analysis, and model evaluation.\n
\n\n ? Apply appropriate ML techniques for prediction, classification, or optimization use cases.\n
\n\n \n ML Workflow Orchestration\n \n
\n\n ? Use Argo Workflows on Kubernetes to orchestrate model inference and post-processing pipelines.\n
\n\n ? Design repeatable, automated workflows for ML experiments and production inference.\n
\n\n \n Model Training & Validation\n \n
\n\n ? Leverage Vertex AI to run scalable model training, hyperparameter tuning, and validation.\n
\n\n ? Ensure reproducibility and consistency across training runs.\n
\n\n \n Model Runtime & Inference\n \n
\n\n ? Build and maintain Python-based runtimes for training, inference, and feature engineering.\n
\n\n ? Optimize inference pipelines for performance, reliability, and scalability.\n
\n\n ? Handle and monitor pipelines in production environments..\n
\n\n ? Participate in Oncall rotation for inference infrastructure.\n
\n\n \n Model Storage & Lifecycle Management\n \n
\n\n ? Handle trained artifacts in Google Cloud Storage (GCS).\n
\n\n ? Track model metadata, versions, and lineage using Argo or custom model registries.\n
\n\n ? Support model versioning, rollback, and auditability..\n
\n\n \n Quality, Monitoring & Governance\n \n
\n\n ? Define model evaluation metrics and validation criteria.\n
\n\n ? Support post-deployment monitoring, drift detection, and retraining strategies.\n
\n\n ? Follow best practices for documentation, testing, and responsible AI usage.\n
\n\n
\n
\n \n Required Skills & Qualifications\n \n
\n\n
\n
\n \n Technical Skills\n \n
\n\n ? Strong on-hands proficiency in Python for data science and machine learning use-cases.\n
\n\n ? Hands-on experience with ML frameworks (e.g., PyTorch, scikit-learn, catboost, …).\n
\n\n ? Familiarity with Kubernetes and Kubernetes-based workflows, especially Argo Workflows.\n
\n\n
\n
\n \n Data Science & ML Concepts\n \n
\n\n ? Strong understanding of feature engineering, model evaluation, and validation techniques.\n
\n\n ? Experience taking models from experimentation to production inference.\n
\n\n ? Understanding of ML lifecycle management and MLOps principles.\n
\n\n
\n
\n \n Soft Skills\n \n
\n\n ? Strong analytical and problem-solving mindset.\n
\n\n ? Ability to translate business problems into data science solutions.\n
\n\n ? Clear communication skills to explain models and results to diverse stakeholders.\n
\n\n
\n
\n \n Nice to Have\n \n
\n\n ? Experience with real-time or batch inference systems.\n
\n\n ? Exposure to CI/CD for ML pipelines.\n
\n\n ? Familiarity with model monitoring, drift detection, and retraining strategies.\n
","employment_type":null,"industry":"","job_template":false,"country":{"name":"India","country_code":"IN","region":"Asia"},"is_job_displayable":true,"job_url":"https://talent500.com/jobs/xebia/data-scientist-hyderabad-T500_CMP811_15820_4419874600","created_at":"2026-06-02T20:50:02.388537+05:30","updated_at":"2026-06-04T21:00:03.143500+05:30","title_alias_1":null,"slug":"data-scientist-hyderabad-T500_CMP811_15820_4419874600","summary":null,"typical_workday":null,"what_you_offer":null,"what_you_need_to_succeed":null,"responsibilities":null,"type":null,"min_experience_years":6,"max_experience_years":8,"ctc_unit":"LPA","is_featured_job":false,"is_active":true,"job_code":"T500_CMP811_15820_4419874600","external_id":null,"external_job_code":"15820_4419874600","is_anonymized":false,"mercurial_skills":null,"open_date":"2026-06-02T20:50:02.380718+05:30","published_at":"2026-06-04T12:50:20.046000+05:30","anonymization_needed":null,"anonymized_description":null,"xml_feed_anonymization":null,"unregistered_user_anonymization":null,"registered_user_anonymization":null,"pre_application_email_anonymization":null,"mettl_account":null,"job_table_sub_head":null,"job_table_short_desc":null,"is_remote":false,"job_video":null,"job_video_title":null,"job_video_description":null,"job_image_url":null,"role_summary":null,"syndication_control":true,"syndication_status":"new","syndication_job_title_alias":null,"sourcing_requirements_updated_at":null,"t500_sourcing_control":true,"status":"open","job_redirection_url":"https://click.appcast.io/t/WjO1LV6zLdu143BI6V392kLGK55PFxylGFOQkSsv4jTIdfmqwtTLiIBBzGNtzo26Loq4eP2SR2k0jfmLp-5fmQ==","is_external_job":true,"is_leadership_job":false,"leadership_confidentiality_level":null,"pricing_type":"cpc","is_auto_apply_eligible":false,"external_apply_url":null,"ats_provider":null,"apply_timeout_seconds":null,"recruiter":"1154daea-f24c-4f7f-aad6-494543ee6112","category":18,"job_sub_category":103,"role":null,"job_function":null,"job_sub_function":null,"naukri_industry":null,"naukri_functional_area":null,"naukri_role":null,"role_category":null,"linkedin_industry":null,"syndication_partners":[],"candidate_job_status":null,"candidate_job_id":null,"mettl_assessment_to_be_taken":false,"company_font_data":{"font-family":"Montserrat","url":"https://fonts.googleapis.com/css2?family=Montserrat:ital,wght@0,100;0,200;0,300;0,400;0,500;0,600;0,700;0,800;0,900;1,100;1,200;1,300;1,400;1,500;1,600;1,700;1,800;1,900&display=swap"}}