Senior Data Scientist - Machine Learning

Senior Machine Learning Engineer

Senior AI Engineer

Data Engineer

Senior Data Scientist 2

Senior Business Intelligence Analyst

Associate Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Data & Business Intelligence Analyst

Staff Engineer - AI/ML

Responsibilities: <ul><li>Lead the design, development, and deployment of machine learning models using Python for applications including process optimization, predictive analytics, demand forecasting, computer vision, and decision support systems</li><li>Network across manufacturing, R&amp;D, commercial, and business functions to identify and collaboratively execute on high-impact challenges solvable through machine learning techniques</li><li>Build production-ready ML solutions within enterprise platforms (Databricks, Azure ML) including model training, endpoint deployment, and monitoring</li><li>Develop and maintain scalable, modular, well-documented code following MLOps best practices including version control, logging/metadata standards, and CI/CD principles</li><li>Lead code reviews, architectural discussions, and knowledge sharing to maintain high quality, production-ready deliverables across the team</li><li>Collaborate with domain experts and stakeholders across a wide variety of domains to understand needs, integrate ML solutions into workflows, and deliver measurable business value</li><li>Communicate complex machine learning methodology, results, and business impact clearly to technical and non-technical audiences through presentations, documentation, and visualizations</li><li>Stay current with advancements in machine learning, and apply them to solve business challenges</li><li>Mentor and provide technical guidance to data scientists, helping to grow the teams capabilities in Hyderabad</li><li>Support ML governance processes including architecture and methodology reviews, risk assessments, and compliance with enterprise standards</li><li>Lead project planning and execution, managing timelines, resources, and stakeholder expectations</li></ul> Requirements: <ul><li>Masters or Ph.D. in a relevant field such as Data Science, Statistics, Mathematics, Computer Science, Operations Research, or a domain-focused field (Engineering, Chemistry, Economics, Finance, etc.)</li><li>5-8+ years of experience in data science, machine learning, or a related field with demonstrated impact</li><li>Strong foundation in statistical and machine learning theory with proven ability to select, apply, and evaluate appropriate techniques for complex, real-world problems</li><li>Extensive hands-on experience building predictive models and deploying them to production environments using Python</li><li>Proficiency with modern ML frameworks (scikit-learn, XGBoost/CatBoost, TensorFlow/PyTorch) and data manipulation libraries (pandas, polars, numpy)</li><li>Experience with solution architecting and design for ML systems</li><li>Hands-on experience with cloud platforms such as Azure and/or Databricks for deploying and managing ML models and solutions</li><li>Experience with version control (Git), collaborative development, and code review processes</li><li>&nbsp;Strong written and oral communication skills with ability to present technical concepts and results to diverse audiences from technical teams to business leadership</li><li>History of project leadership and driving initiatives from conception to production</li><li>Strong problem-solving skills and the ability to work independently as well as lead and mentor team members&nbsp;</li></ul>

Role Description: <ul><li>Working closely with the Data Scientists and be responsible for implementing large language models (LLMs) based applications, working with proprietary and open-source Models as well as popular frameworks such as Lang Chain to ensure seamless integration and deployment.</li></ul> Responsibilities: <ul><li>Building standalone applications that interact with LLM models.</li><li>Building RAG-based applications.</li><li>Understanding of Vector DBs like Solr for LLM based applications</li><li>Integrate models with existing systems and APIs.</li><li>Implement production quality ETL jobs</li><li>Preprocess and manage data for training and deployment.</li><li>Collaborate with cross-functional teams to define, design, and ship new features.</li><li>Write clean, maintainable, and efficient code.</li><li>Document development processes, code, and APIs.</li></ul> Requirements: <ul><li>Degree in Computer Science, Data Science, or related field.• Proven experience in building customer facing ML based APIs</li><li>Mexico City–based (or nearby), available to work onsite 2 days per week at Glorietadel Ahuehuete, Reforma.</li><li>Experience in developing applications that are scalable to handle TBs of data</li><li>Strong knowledge of API integration (RESTful, GraphQL).</li><li>Experience with data preprocessing, SQL, and NoSQL databases as well as vector stores (e.g., Postgres, MySQL, Solr, Elasticsearch/OpenSearch, etc.).</li><li>Familiarity with deployment tools (Docker, Kubernetes).</li><li>Experience with DevOps tools like Jenkins, Terraform, Cloud Formation templates is highly preferred.</li><li>Excellent problem-solving and communication skills.</li><li>Experience with Spark/Hadoop, EMR or any other Big Data technology would be a plus</li><li>Experience working on agents is considered a good-to-have</li><li>Familiarity with agents, even through courses Work in a way that works for you We promote a healthy work/life balance across the organization..</li></ul> Other Information: These are some of the benefits we are delighted to offer: <ul><li>Private Medical/Dental Plan.</li><li>Savings Fund.</li><li>Life Insurance.</li><li>Meal/Grocery Voucher.</li><li>We offer an appealing working prospect for our people.</li><li>With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.</li><li>Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive Working for you.</li><li>We know that your well-being and happiness are key to a long and successful career.</li></ul>

Role Description: <ul><li>We are building a new data engineering team to support a strategic move from legacy data estates to a modern data platform built on Databricks.</li><li>This role plays a key part in re-engineering existing systems, improving scalability, and enabling future innovation.</li></ul> Responsibilities: <ul><li>Transition legacy data systems to Databricks.</li><li>Build and maintain robust, scalable data pipelines.</li><li>Work closely with subject matter experts, product teams, and stakeholders to understand business logic.</li><li>Apply best practices in data engineering and programming.</li><li>Mentor junior engineers and contribute to knowledge sharing.</li><li>Participate in code reviews and technical discussions.</li></ul> Requirements: <ul><li>Hands-on experience in software or data engineering roles.</li><li>Strong proficiency in Python and working knowledge of PySpark.</li><li>Experience or strong interest in Databricks.</li><li>Solid understanding of SQL and structured data.</li><li>Experience with cloud platforms (AWS preferred; Azure/GCP acceptable).</li><li>Familiarity with REST APIs and event-driven architectures.</li><li>Experience using Git or similar version control systems.</li><li>Strong problem-solving skills and collaborative mindset.</li></ul> Preferred Experience <ul><li>Experience working on data platform modernization or migration projects.</li><li>Ability to refactor and improve legacy codebases.</li><li>Exposure to AI/ML data pipelines is an advantage.</li></ul> Why Join Us <ul><li>Opportunity to work on a strategic, high-impact data platform transformation.</li><li>Exposure to cutting-edge technologies including Databricks and cloud platforms.</li><li>Collaborative, inclusive team culture with strong mentorship.</li><li>Clear opportunities for career growth and skill development.</li></ul>

Role Overview: <ul><li>We are building a new data engineering team to support a strategic move from legacy data estates to a modern data platform built on Databricks.&nbsp;</li><li>This role plays a key part in re-engineering existing systems, improving scalability, and enabling future innovation.</li></ul> Responsibilities: <ul><li>Lead the transition of legacy data systems to Databricks, ensuring reliability and performance.</li><li>Design, build, and maintain robust, scalable data pipelines to support analytical and operational use cases.</li><li>Collaborate closely with subject matter experts, product teams, and business stakeholders to understand and implement business logic accurately.</li><li>Apply data engineering and programming best practices to ensure high-quality, maintainable, and secure solutions.</li><li>Mentor junior engineers, fostering technical growth and promoting knowledge sharing across the team.</li><li>Participate in code reviews, architectural discussions, and technical decision-making to uphold engineering standards.</li></ul> Requirements:&nbsp; Required Skills &amp; Experience: <ul><li>Hands-on experience in software or data engineering roles.</li><li>Strong proficiency in Python and working knowledge of PySpark.</li><li>Experience or strong interest in Databricks.</li><li>Solid understanding of SQL and structured data.</li><li>Experience with cloud platforms (AWS preferred; Azure/GCP acceptable).</li><li>Familiarity with REST APIs and event-driven architectures.</li><li>Experience using Git or similar version control systems.</li><li>Strong problem-solving skills and collaborative mindset.</li></ul> Preferred Experience: <ul><li>Experience working on data platform modernization or migration projects.</li><li>Ability to refactor and improve legacy codebases.</li><li>Exposure to AI/ML data pipelines is an advantage.</li></ul>

Responsibilities: <ul><li>Lead the design and development of LLM-powered solutions for clinical summarization, intelligent chat, and document understanding to improve patient care outcomes.</li><li>Architect and implement end-to-end machine learning pipelines, including Retrieval-Augmented Generation (RAG) systems using vector databases such as Pinecone and Weaviate.</li><li>Fine-tune and optimize state-of-the-art language models (e.g., GPT, LLaMA, Mistral) and apply advanced prompt engineering to address specialty-specific healthcare use cases.</li><li>Own project delivery end to end, collaborating with stakeholders to ensure AI solutions are ethical, scalable, reliable, and well-governed.</li><li>Mentor and support junior data scientists, fostering a culture of experimentation, learning, and innovation.</li></ul> Requirements: <ul><li>Experience: 8–12 years in Data Science/ML, with at least 3+ years specifically focused on LLMs and Generative AI.&nbsp;</li><li>Technical Mastery: Deep hands-on expertise in Python and frameworks like PyTorch, Hugging Face, LangChain, or LlamaIndex.&nbsp;</li><li>Cloud Fluency: Proven ability to deploy models at scale using AWS SageMaker, Azure ML, or GCP Vertex AI.&nbsp;</li><li>MLOps Mindset: Experience with MLflow, Airflow, Docker, and Kubernetes to ensure our "bold innovations" are also stable and reliable.&nbsp;</li><li>Academic Foundation: A strong grasp of statistical analysis and evaluation techniques to mitigate hallucinations and ensure clinical accuracy.&nbsp;</li></ul>

Role Description: <ul><li>The Continuous Improvement EMIA team reports to Operations Automotive EMIA.</li><li>By providing new generation digital solutions to our Operational teams across EMIA, our team creates full transparency and speed in the ongoing digital &amp; lean transformation(s), which will enable all involved functions to leverage data to develop business insights and roadmap updates based upon clearly arranged and understandable data. Due to the extended scope, focusing also on the connecting processes from Supplier(s) to Manufacturing ending at the Customer(s) (internal and external) we will function as ‘the’ differentiator for our Company.</li><li>The Data Scientist Automotive EMIA will report to the Smart Factory Leader Automotive EMIA.&nbsp;</li></ul> Responsibilities:&nbsp; <ul><li>Lead projects and work together with our Automotive EMIA Operations plants, internal (IT, , Corporate Technology, CoE, SC, ..) and external partners in the area of Smart Factory (Applications, ML, AI, DL), aligned with Operations Automotive EMIA Strategy.</li><li>Lead Smart Factory improvement projects (Enhancements) of existing applications together with internal / external supplier and customers (project management) until the level of RTD (Ready To Deploy)</li><li>·Use a combined knowledge of computer science and applications, modelling, statistics, analytics and maths to solve problems.</li><li>Identify and interpret data sources, manage large amounts of data, merge data sources together, ensure consistency of datasets, create visualizations to aid in understanding data.</li><li>Develop, Validate and Deploy (including Training and Coaching of the users) until the level of RTD (Ready To Deploy), Digital Factory Applications, ML models, contributing to waste elimination and efficiency improvements within all Operations functions (Manufacturing, Quality, SC, LOG, ….), this together with our internal and external partners.</li></ul>&nbsp;Requirements: Desired candidate &amp; Qualification <ul><li>Good understanding of a technology driven manufacturing company.</li><li>Knowledge of the core manufacturing technologies: Stamping, Plating, Assembly, Molding.</li><li>Experienced with data visualization tools like PowerBi, Tableau, ThingWorx,..</li><li>Good applied statistics skills, such as distributions, statistical testing, regression, etc.</li><li>Development experience in programming languages R, Python.</li><li>Knowledge, experience in JAVA scripting</li><li>Excellent pattern recognition and predictive modelling skills.</li><li>Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, neural networks and deep learning etc.</li><li>Experience in technologies like, Jupyter Notebooks, Numpy, Pandas, Seaborn, Scikit-learn, PyTorch and TensorFlow.</li><li>Exposure to recent developments in Deep Learning, Gen AI, Agentic AI domain.</li><li>Database skills, on-premises and cloud based.</li><li>Experience in AWS is an advantage, functional knowledge of AWS platforms such as Sagemaker, S3, and RedShift.</li><li>Excellent presentation skills in both spoken en written English.</li><li>&nbsp;Excellent and strong communicator</li><li>The ability to translate, explain and bring, complex data science topics to an easy understandable level, speeding up acceptance level in general.</li><li>Bachelor or master’s in data science</li><li>Team player with a solution focused problem-solving mentality.</li><li>Change agent with high Execution level, including training and coaching talents.</li><li>Passioned about the combination of Technology, Data, Analytics and Statistics with link to Quality, Product and Process Engineering</li><li>Inspired by and searching actively for new technologies and methods.</li></ul>

Responsibilities: <ul><li>Ability to troubleshoot problems in a live environment and provide real time help with critical issues.</li><li>Write code that is easily understood and maintainable by other team members.</li><li>Keep up to date with developments in technologies we are using.</li><li>Ability to communicate and work effectively, supporting various business departments.</li><li>Ability to work in fast-paced interdisciplinary environment.</li></ul> Requirements <ul><li>BA/BS in Computer Science or equivalent practical experience.&nbsp;</li><li>At least 3+ years of post-university experience as a full-stack engineer.</li><li>Solid knowledge of programming fundamentals—algorithms, data structures, design patterns, and paradigms.</li><li>Strong knowledge of Python.</li><li>Solid knowledge SQL and relational databases.</li><li>Knowledge of Extract-Transform-Load (ETL) and big data analytics tools.</li><li>Experience working in a cloud environment (AWS, Azure, GCP).</li><li>Familiarity with popular Python data analytics frameworks.</li><li>Familiarity with Snowflake or other large analytics engines.</li><li>Experience with data warehousing and working in environments with large scale, complex analytics requirements.</li><li>Familiarity with DAG-based job scheduling tools.</li><li>Cloud-native container orchestration platforms and tools.</li><li>Experience working in the Financial Services industry.</li></ul>

Responsibilities: <ul><li>Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.&nbsp;</li><li>Build and scale ML pipelines using Vertex AI Pipelines, Kubeflow, Airflow, and manage infra-as-code with Terraform/Helm.&nbsp;</li><li>Implement automated retraining, drift detection, and re-deployment of ML models.&nbsp;</li><li>Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.&nbsp;</li><li>Implement model monitoring, observability, and alerting across accuracy, latency, and cost.&nbsp;</li><li>Integrate and manage feature stores, knowledge graphs, and vector databases for advanced ML/RAG use cases.&nbsp;</li><li>Ensure pipelines are secure, compliant, and cost-optimized.&nbsp;&nbsp;</li><li>Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance .&nbsp;</li><li>Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.&nbsp;</li><li>Stay current with the latest ML research and apply new tools pragmatically to production systems.&nbsp;</li><li>Collaborate with product managers, DS, and engineers to translate business problems into reliable ML systems.&nbsp;</li></ul> Requirements: <ul><li>Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).&nbsp;</li><li>5+ years of experience in machine learning engineering, MLOps, or large-scale AI/DS systems.&nbsp;</li><li>Strong foundations in data structures, algorithms, and distributed systems.&nbsp;</li><li>Proficient in Python (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.&nbsp;</li><li>Hands-on experience building and deploying ML models at scale in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).&nbsp;</li><li>Experience with containerization (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).&nbsp;</li><li>Familiarity with CI/CD pipelines, infrastructure-as-code (Terraform/Helm), and configuration management.&nbsp;</li><li>Experience with big data and streaming technologies (Spark, Flink, Kafka, Hive, Hadoop).&nbsp;</li><li>Practical exposure to model observability tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)&nbsp;</li><li>Strong understanding of statistical methods, ML algorithms, and deep learning architectures.&nbsp;</li></ul>

Responsibilities: <ul><li>Dashboard Design and Development: Design, develop, and maintain interactive and user-friendly dashboards in PowerBI and Looker, focusing on clarity, performance, and data accuracy.&nbsp;</li><li>Data Lake Integration: Connect to and leverage data from various data lake sources on GCP, ensuring seamless data flow for visualization purposes.&nbsp;</li><li>Semantic Model Development: Develop and maintain robust semantic models in Looker (LookML) and PowerBI, ensuring data consistency, accuracy, and ease of use for reporting and analysis. This includes defining dimensions, measures, relationships, and calculations.&nbsp;</li><li>Machine Learning and GenAI Visualization: Create compelling visualizations to effectively communicate the results, performance, and insights derived from machine learning models and GenAI applications. This may involve visualizing model predictions, feature importance, natural language processing outputs, and other relevant data.&nbsp;</li><li>GCP Expertise: Utilize your strong understanding of GCP services to access, process, and prepare data for visualization. This may involve working with services such as BigQuery, Cloud Storage, and potentially data engineering pipelines built on GCP.&nbsp;</li><li>Collaboration: Partner closely with data scientists, data engineers, product managers, and business stakeholders to understand their data visualization needs and translate them into effective visual solutions.&nbsp;</li><li>Requirements Gathering: Elicit and document visualization requirements from various stakeholders, ensuring alignment with business objectives.&nbsp;</li><li>Performance Optimization: Optimize dashboard and visualization performance to ensure fast load times and efficient data rendering.&nbsp;</li><li>Data Quality Assurance: Ensure the accuracy, consistency, and reliability of data presented in visualizations.&nbsp;</li><li>Best Practices and Standards: Establish and enforce data visualization best practices and standards across the organization.&nbsp;</li><li>Training and Support: Provide guidance and support to other team members on data visualization tools and techniques.&nbsp;</li><li>Continuous Learning: Stay up to date with the latest trends and advancements in data visualization, PowerBI, Looker, GCP, and related technologies, particularly in the context of AI/ML.&nbsp;</li></ul>&nbsp;Requirements: <ul><li>Bachelor’s or master’s degree in computer science, Data Science, Statistics, Information Systems, or a related field.&nbsp;</li><li>Minimum of 3 years of experience as a Visualization Expert, Data Visualization Engineer, or a similar role.&nbsp;</li><li>Proven expertise in designing and developing interactive dashboards and reports using PowerBI and Looker.&nbsp;</li><li>Strong experience in connecting to and working with data lakes, preferably on the Google Cloud Platform (GCP).&nbsp;</li><li>Solid understanding and hands-on experience in developing semantic models, particularly with LookML in Looker and data modeling within PowerBI.&nbsp;</li><li>Preferable experience in creating visualizations specifically for machine learning and GenAI applications, understanding the nuances of presenting model outputs and insights.&nbsp;</li><li>Proficiency in SQL for data extraction and manipulation.&nbsp;</li><li>Familiarity with data warehousing concepts and data modeling techniques.&nbsp;</li><li>Experience with data preparation, ELT (preferred) / ETL processes.&nbsp;</li><li>Strong analytical and problem-solving skills with the ability to interpret complex data.&nbsp;</li><li>Excellent communication and presentation skills, with the ability to convey technical information to both technical and non-technical audiences.&nbsp;&nbsp;</li><li>Ability to work independently and collaboratively in a fast-paced environment.&nbsp;&nbsp;</li><li>Strong portfolio showcasing your data visualization skills and experience.&nbsp;</li></ul>

Role Description: <ul><li>As a Staff Engineer, you will play a pivotal role in shaping the future of Gen AI at our client by defining technical strategy, overseeing complex machine learning architectures, and ensuring seamless integration of Gen AI solutions into our platform and applications at scale.</li><li>You will mentor and guide a small engineering team while providing expertise in state-of-the-art AI/ML practices, ensuring a robust production pipeline and the professional management of models.&nbsp;</li></ul> Responsibilities: <ul><li>Help lead the technical strategy and execution for AI projects across the organization.</li><li>Designing, deploying, and managing the lifecycle of AI/ML models, including retraining and monitoring for continuous improvement within our solution.</li><li>Designing and driving Gen AI features on peta-byte scale datasets Driving innovation in NLP and other ML solutions, ensuring alignment with our client business goals. Architecting, developing and overseeing scalable, production-ready AI systems.</li><li>Providing technical mentorship to the AI/ML engineering team, fostering growth and collaboration.</li><li>Conducting in-depth research and staying abreast of advancements in Gen AI and deep learning technologies.</li><li>Collaborating with cross-functional teams, including Product, to evaluate business requirements and determine optimal AI solutions.</li><li>Reviewing code, offering best practices, and ensuring the delivery of high-quality solutions.&nbsp;</li><li>Representing our client technical excellence through publications, presentations, and participation in industry events.</li><li>Opportunity to work on cutting-edge AI projects at scale.</li></ul> Requirements: <ul><li>8+ years experience in managing large-scale datasets and distributed computing environments, with a preference for Google Cloud Platform expertise.</li><li>Java programming experience.</li><li>Experience with integration and deployment of AI/ML into production at scale within a SaaS solution.</li><li>Demonstrates independent thinking, capable of analyzing unique challenges, overcoming technical hurdles and delivering impactful AI solutions.</li><li>Hands-on experience with Generative AI techniques, including RAG pipelines, LLM fine-tuning, prompt engineering, and diffusion models.</li><li>Strong knowledge of generative AI frameworks and tools, such as Hugging Face, LangChain, or OpenAI APIs.</li><li>Experience in developing and scaling Generative AI solutions for NLP, vision, or multimodal tasks.</li><li>Advanced knowledge of modern AI frameworks, such as PyTorch, TensorFlow/Keras, Hugging Face Transformers, and JAX/Flax.</li><li>Expertise in Python, Docker, Kubernetes, and Helm.</li><li>Excellent communication skills, particularly in mentoring, knowledge sharing, and cross-team collaboration.</li><li>Willingness to be in office 3 days per week.</li></ul>

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21 ml algorithms Jobs found

Senior Data Scientist - Machine Learning

Senior Machine Learning Engineer

Senior AI Engineer

Data Engineer

Senior Data Scientist 2

Senior Business Intelligence Analyst

Associate Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Data & Business Intelligence Analyst

Staff Engineer - AI/ML

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21 ml algorithms Jobs found

Senior Data Scientist - Machine Learning

Senior Machine Learning Engineer

Senior AI Engineer

Data Engineer

Senior Data Scientist 2

Senior Business Intelligence Analyst

Associate Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

Data & Business Intelligence Analyst

Staff Engineer - AI/ML

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