business analyst

data analyst

ai data scientist

ai/ml specialist

data engineer

data scientist

Datamatics Technologies

<div>
 
 
 Job Description: Data Scientist (MLOps, CI/CD, Generative AI)&nbsp;
 
 Experience : 4 to 6 Years
 
 Location: Isb / Hybrid
 
 
 
 About the Role&nbsp;
 
 
 We are seeking a Full Stack Data Scientist with strong expertise in MLOps, CI/CD, and Generative AI to join our growing AI/ML team. This role is highly dynamic and requires a balance of technical excellence, customer-facing skills, and business acumen.&nbsp;
 
 
 You will be responsible for the end-to-end machine learning lifecycle&mdash;from data exploration and model development to deployment and optimization. A key part of the role is engaging with customers, understanding their business challenges, and delivering impactful Proof of Concepts (POCs) that build trust and demonstrate value.&nbsp;
 
 
 Candidates with hands-on expertise in statistical models, traditional ML, deep learning architectures, time series forecasting, and financial modeling will be strongly preferred.&nbsp;
 
 
 Experience with GPUs, CUDA, and high-performance computing is a plus, given the scale and complexity of deep learning and Generative AI workloads.&nbsp;
 
 
 Key Responsibilities&nbsp;
 
 <ul>
 <li>
 
 Engage directly with customers to understand business problems and translate them into data science solutions.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Design and deliver impactful projects that clearly demonstrate value and help win new business opportunities.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Build and deploy end-to-end ML/AI solutions across domains such as NLP, Computer Vision, Generative AI, and Forecasting.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Develop and optimize MLOps pipelines for model training, deployment, and monitoring with CI/CD best practices.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Implement statistical, traditional ML, and deep learning models, ensuring accuracy, scalability, and robustness.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Create time series and financial forecasting models for predictive analytics in business and finance use cases.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Apply Generative AI methods (LLMs, RAG, LangChain, Hugging Face, Diffusion Models) to enterprise use cases.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Optimize training and inference with GPU acceleration and CUDA where applicable.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Ensure production-grade deployment with monitoring, drift detection, and retraining strategies.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Collaborate with product managers, engineers, and stakeholders to align technical solutions with business outcomes.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Stay updated with industry trends and bring innovative AI/ML solutions to customer engagements.&nbsp;
 
 </li>
 </ul>
 
 Required Qualifications&nbsp;
 
 <ul>
 <li>
 
 Education: Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Data Science, AI/ML, or related field.&nbsp;
 
 </li>
 </ul>
 
 
 <ul>
 <li>
 
 Experience: 3+ years of professional experience in end-to-end ML/AI solution delivery.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Technical Expertise:&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Statistical models (hypothesis testing, regression, time series analysis).&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Traditional ML (SVM, decision trees, ensemble methods, clustering, recommendation systems).&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Deep Learning (CNNs, RNNs, LSTMs/GRUs, Transformers, GANs, Diffusion Models).&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Time Series &amp; Financial Models (ARIMA, Prophet, advanced LSTM/GRU models, risk prediction).&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Generative AI (LLMs, RAG, LangChain, Hugging Face Transformers, OpenAI APIs).&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Proficiency in Python (NumPy, Pandas, Scikit-learn, Statsmodels, TensorFlow, PyTorch).&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Hands-on experience with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI).&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Strong knowledge of CI/CD workflows (GitHub Actions, GitLab CI, Jenkins, Azure DevOps).&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Experience with cloud platforms (AWS, Azure, GCP) for ML deployment and scaling.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Strong understanding of Docker, Kubernetes, and production deployment.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Excellent communication and presentation skills to face customers confidently.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Proven ability to translate customer requirements into POCs and production solutions..&nbsp;
 
 </li>
 </ul>
 
 Preferred/Bonus Skills&nbsp;
 
 <ul>
 <li>
 
 Experience with GPUs, CUDA, and high-performance model training.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Familiarity with real-time inference frameworks (TensorRT, Triton, TorchServe, FastAPI).&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Knowledge of feature stores (Feast, Tecton) and monitoring tools (Evidently, WhyLabs, Prometheus, Grafana).&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Exposure to financial services, supply chain, or enterprise AI domains.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Track record of winning client trust through successful POCs and solution delivery.&nbsp;
 
 </li>
 </ul>
 <ul>
 <li>
 
 Contributions to open-source ML/AI projects.&nbsp;
 
 </li>
 </ul>
</div>

Data Scientist

Datamatics Global Services Ltd

<div>Job Description: Data Scientist (MLOps, CI/CD, Generative AI) 
Experience : 4 to 6 Years 
Location: Isb / Hybrid 
 
About the Role
We are seeking a Full Stack Data Scientist with strong expertise in MLOps, CI/CD, and Generative AI to join our growing AI/ML team. This role is highly dynamic and requires a balance of technical excellence, customer-facing skills, and business acumen.
You will be responsible for the end-to-end machine learning lifecycle&mdash;from data exploration and model development to deployment and optimization. A key part of the role is engaging with customers, understanding their business challenges, and delivering impactful Proof of Concepts (POCs) that build trust and demonstrate value.
Candidates with hands-on expertise in statistical models, traditional ML, deep learning architectures, time series forecasting, and financial modeling will be strongly preferred.
Experience with GPUs, CUDA, and high-performance computing is a plus, given the scale and complexity of deep learning and Generative AI workloads.
Key Responsibilities<ul><li>
Engage directly with customers to understand business problems and translate them into data science solutions.</li><li>
Design and deliver impactful projects that clearly demonstrate value and help win new business opportunities.</li><li>
Build and deploy end-to-end ML/AI solutions across domains such as NLP, Computer Vision, Generative AI, and Forecasting.</li><li>
Develop and optimize MLOps pipelines for model training, deployment, and monitoring with CI/CD best practices.</li><li>
Implement statistical, traditional ML, and deep learning models, ensuring accuracy, scalability, and robustness.</li><li>
Create time series and financial forecasting models for predictive analytics in business and finance use cases.</li><li>
Apply Generative AI methods (LLMs, RAG, LangChain, Hugging Face, Diffusion Models) to enterprise use cases.</li><li>
Optimize training and inference with GPU acceleration and CUDA where applicable.</li><li>
Ensure production-grade deployment with monitoring, drift detection, and retraining strategies.</li><li>
Collaborate with product managers, engineers, and stakeholders to align technical solutions with business outcomes.</li><li>
Stay updated with industry trends and bring innovative AI/ML solutions to customer engagements.</li></ul>
Required Qualifications<ul><li>
Education: Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Data Science, AI/ML, or related field.</li></ul><ul><li>
Experience: 3+ years of professional experience in end-to-end ML/AI solution delivery.</li><li>
Technical Expertise:</li><li>
Statistical models (hypothesis testing, regression, time series analysis).</li><li>
Traditional ML (SVM, decision trees, ensemble methods, clustering, recommendation systems).</li><li>
Deep Learning (CNNs, RNNs, LSTMs/GRUs, Transformers, GANs, Diffusion Models).</li><li>
Time Series &amp; Financial Models (ARIMA, Prophet, advanced LSTM/GRU models, risk prediction).</li><li>
Generative AI (LLMs, RAG, LangChain, Hugging Face Transformers, OpenAI APIs).</li><li>
Proficiency in Python (NumPy, Pandas, Scikit-learn, Statsmodels, TensorFlow, PyTorch).</li><li>
Hands-on experience with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI).</li><li>
Strong knowledge of CI/CD workflows (GitHub Actions, GitLab CI, Jenkins, Azure DevOps).</li><li>
Experience with cloud platforms (AWS, Azure, GCP) for ML deployment and scaling.</li><li>
Strong understanding of Docker, Kubernetes, and production deployment.</li><li>
Excellent communication and presentation skills to face customers confidently.</li><li>
Proven ability to translate customer requirements into POCs and production solutions..</li></ul>
Preferred/Bonus Skills<ul><li>
Experience with GPUs, CUDA, and high-performance model training.</li><li>
Familiarity with real-time inference frameworks (TensorRT, Triton, TorchServe, FastAPI).</li><li>
Knowledge of feature stores (Feast, Tecton) and monitoring tools (Evidently, WhyLabs, Prometheus, Grafana).</li><li>
Exposure to financial services, supply chain, or enterprise AI domains.</li><li>
Track record of winning client trust through successful POCs and solution delivery.</li><li>
Contributions to open-source ML/AI projects.</li></ul>
muKNVCbdCc</div>

<div>
 
 Job Title:
 
 AI Data Scientist (Offshore)
 
 
 
 Duration:
 
 6 Months (Extendable)
 
 
 Experience:
 
 &nbsp;6&ndash;8 Years
 
 
 Location:
 
 Offshore (Remote)
 
 
 Industry:
 
 Multi-Industry (with focus on Financial Sector)
 
 
 Employment Type:
 
 Contract (6 Months - Higher possiblity for extension)
 
 
 
 Job Summary
 
 
 We are seeking a highly skilled
 
 AI Data Scientist
 
 with hands-on experience in building and deploying advanced data science and machine learning models, particularly within the
 
 financial services domain
 
 . The ideal candidate will have strong proficiency in
 
 Python, SQL
 
 , and
 
 Google Cloud Platform (GCP)
 
 tools &mdash; leveraging platforms like
 
 BigQuery, Vertex AI, Dataflow, Dataproc, Pub/Sub, GCS, and Looker
 
 to deliver scalable and automated AI solutions.
 
 
 This is an exciting
 
 6-month contract role (with potential extension)
 
 focused on developing
 
 descriptive, predictive, and prescriptive models
 
 and deploying them in production using
 
 MLOps best practices
 
 .
 
 
 
 Key Responsibilities
 
 <ul>
 <li>
 
 Design and develop
 
 data science and machine learning models
 
 across descriptive, predictive, and prescriptive analytics.
 
 </li>
 <li>
 
 Work closely with business stakeholders&mdash;especially in the
 
 financial sector
 
 &mdash;to translate complex problems into AI-driven solutions.
 
 </li>
 <li>
 
 Develop and automate
 
 end-to-end ML pipelines
 
 on
 
 GCP
 
 using Vertex AI, Dataflow, Pub/Sub, and Dataproc.
 
 </li>
 <li>
 
 Implement
 
 MLOps
 
 frameworks for continuous integration, deployment, and monitoring of machine learning models.
 
 </li>
 <li>
 
 Analyze large datasets using
 
 BigQuery
 
 and other GCP-native tools for data transformation, feature engineering, and model training.
 
 </li>
 <li>
 
 Visualize insights and performance metrics using
 
 Looker
 
 and other BI tools.
 
 </li>
 <li>
 
 Collaborate with data engineers, analysts, and cloud architects to ensure seamless model deployment and scalability.
 
 </li>
 <li>
 
 Evaluate model performance and refine algorithms for improved accuracy and business impact.
 
 
 </li>
 </ul>
 
 Required Skills &amp; Qualifications
 
 <ul>
 <li>
 
 
 5&ndash;8 years
 
 of professional experience in
 
 data science, AI/ML
 
 , and analytics roles.
 
 </li>
 <li>
 
 Proven expertise in
 
 Python
 
 (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch) and
 
 SQL
 
 for data analysis and model development.
 
 </li>
 <li>
 
 Hands-on experience with
 
 GCP ecosystem
 
 , including:
 
 <ul>
 <li>
 
 
 BigQuery
 
 (data querying and transformation)
 
 </li>
 <li>
 
 
 Vertex AI
 
 (model training, tuning, deployment)
 
 </li>
 <li>
 
 
 Dataflow / Dataproc
 
 (data processing pipelines)
 
 </li>
 <li>
 
 
 Pub/Sub
 
 (event-driven data streaming)
 
 </li>
 <li>
 
 
 GCS (Google Cloud Storage)
 
 and
 
 Looker
 
 (visualization and reporting)
 
 </li>
 </ul>
 </li>
 <li>
 
 Strong knowledge of
 
 MLOps automation
 
 and continuous deployment best practices.
 
 </li>
 <li>
 
 Experience developing models in
 
 financial services
 
 (risk scoring, fraud detection, customer analytics, forecasting, etc.).
 
 </li>
 <li>
 
 Strong understanding of
 
 statistical modeling
 
 ,
 
 feature engineering
 
 , and
 
 model performance evaluation
 
 .
 
 </li>
 <li>
 
 Excellent problem-solving and communication skills with ability to work in multi-industry environments.
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 <ul>
 <li>
 
 Experience in
 
 multi-cloud
 
 or hybrid environments.
 
 </li>
 <li>
 
 Knowledge of
 
 CI/CD pipelines
 
 for ML (GitHub Actions, Jenkins, or Cloud Build).
 
 </li>
 <li>
 
 Exposure to
 
 deep learning frameworks
 
 (TensorFlow, PyTorch).
 
 </li>
 <li>
 
 Experience in
 
 time-series forecasting
 
 ,
 
 credit modeling
 
 , or
 
 financial risk analytics
 
 .
 
 </li>
 </ul>
</div>

AI Data Scientist

<div><h2 class="jobSectionHeader">Job Title: AI Data Scientist (Offshore)</h2>
Duration: 6 Months (Extendable) 
Experience: 6&ndash;8 Years 
Location: Offshore (Remote) 
Industry: Multi-Industry (with focus on Financial Sector) 
Employment Type: Contract (6 Months - Higher possiblity for extension)<h3 class="jobSectionHeader">
Job Summary</h3>
We are seeking a highly skilled AI Data Scientist with hands-on experience in building and deploying advanced data science and machine learning models, particularly within the financial services domain. The ideal candidate will have strong proficiency in Python, SQL, and Google Cloud Platform (GCP) tools &mdash; leveraging platforms like BigQuery, Vertex AI, Dataflow, Dataproc, Pub/Sub, GCS, and Looker to deliver scalable and automated AI solutions.
This is an exciting 6-month contract role (with potential extension) focused on developing descriptive, predictive, and prescriptive models and deploying them in production using MLOps best practices.<h3 class="jobSectionHeader"> 
Key Responsibilities</h3><ul><li>
Design and develop data science and machine learning models across descriptive, predictive, and prescriptive analytics.</li><li>
Work closely with business stakeholders&mdash;especially in the financial sector&mdash;to translate complex problems into AI-driven solutions.</li><li>
Develop and automate end-to-end ML pipelines on GCP using Vertex AI, Dataflow, Pub/Sub, and Dataproc.</li><li>
Implement MLOps frameworks for continuous integration, deployment, and monitoring of machine learning models.</li><li>
Analyze large datasets using BigQuery and other GCP-native tools for data transformation, feature engineering, and model training.</li><li>
Visualize insights and performance metrics using Looker and other BI tools.</li><li>
Collaborate with data engineers, analysts, and cloud architects to ensure seamless model deployment and scalability.</li><li>
Evaluate model performance and refine algorithms for improved accuracy and business impact.</li></ul><h3 class="jobSectionHeader"> 
Required Skills &amp; Qualifications</h3><ul><li>
5&ndash;8 years of professional experience in data science, AI/ML, and analytics roles.</li><li>
Proven expertise in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch) and SQL for data analysis and model development.</li><li>
Hands-on experience with GCP ecosystem, including:<ul><li>
BigQuery (data querying and transformation)</li><li>
Vertex AI (model training, tuning, deployment)</li><li>
Dataflow / Dataproc (data processing pipelines)</li><li>
Pub/Sub (event-driven data streaming)</li><li>
GCS (Google Cloud Storage) and Looker (visualization and reporting)</li></ul></li><li>
Strong knowledge of MLOps automation and continuous deployment best practices.</li><li>
Experience developing models in financial services (risk scoring, fraud detection, customer analytics, forecasting, etc.).</li><li>
Strong understanding of statistical modeling, feature engineering, and model performance evaluation.</li><li>
Excellent problem-solving and communication skills with ability to work in multi-industry environments.</li></ul><h3 class="jobSectionHeader"> 
Preferred Qualifications</h3><ul><li>
Experience in multi-cloud or hybrid environments.</li><li>
Knowledge of CI/CD pipelines for ML (GitHub Actions, Jenkins, or Cloud Build).</li><li>
Exposure to deep learning frameworks (TensorFlow, PyTorch).</li><li>
Experience in time-series forecasting, credit modeling, or financial risk analytics.</li></ul>
olqQlu28No</div>

Information Tech Consultants

<div>
 
 🌟 Senior Data Scientist SME /
 
 Data Science SME (Subject matter expert)
 
 🧠
 
 
 
 
 
 
 Location : London (Relocation required - Sponsorship will be provided)
 
 
 
 
 Experience : 9-12 years
 
 
 
 
 CTC- 40-45 lacs
 
 
 
 
 
 
 We are seeking a highly accomplished and results-oriented
 
 Senior Data Scientist Subject Matter Expert (SME)
 
 with over
 
 10 years of experience
 
 to lead our advanced analytics and AI initiatives. This is a pivotal role requiring deep technical mastery across large-scale Big Data technologies, multi-cloud environments, and cutting-edge specialized AI domains, including
 
 Generative AI, Computer Vision, and Natural Language Processing (NLP)
 
 .
 
 
 You will be the principal technical leader, driving strategy, setting standards, and delivering high-impact solutions that transform business outcomes.
 
 
 
 
 
 Key Responsibilities
 
 
 
 
 <ul>
 <li>
 
 AI/ML Strategy &amp; Architecture:
 
 Define the technical roadmap and architectural standards for deploying and scaling complex AI systems, particularly those involving
 
 Generative AI (GenAI)
 
 , large language models (
 
 LLMs
 
 ), and specialized models in Computer Vision and NLP.
 </li>
 <li>
 
 Big Data Engineering:
 
 Design, build, and optimize high-throughput, distributed data pipelines and features using
 
 Apache Spark (Scala)
 
 and
 
 Hive
 
 on massive datasets to support model training and inference.
 </li>
 <li>
 
 Cross-Cloud Execution:
 
 Lead the design and deployment of ML models and data infrastructure across multiple major cloud providers (
 
 AWS, Azure, and GCP
 
 ), ensuring portability, scalability, and cost efficiency.
 </li>
 <li>
 
 Specialized Model Development:
 
 Lead hands-on development, fine-tuning, and deployment of production-grade models in key specialized areas:
 </li>
 <li>
 
 Computer Vision:
 
 Developing and optimizing models for image recognition, object detection, and video analytics.
 </li>
 <li>
 
 NLP:
 
 Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs.
 </li>
 <li>
 
 Generative AI:
 
 Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product features.
 </li>
 <li>
 
 SME Consulting &amp; Mentorship:
 
 Act as the internal authority and consultant, providing technical guidance, architectural review, and mentorship to junior data scientists and engineering teams.
 </li>
 <li>
 
 MLOps &amp; Governance:
 
 Establish best practices for MLOps, model monitoring, version control, and model risk governance in a multi-cloud production environment.
 </li>
 </ul>
 
 
 
 
 Required Skills and Expertise (10+ Years)
 
 
 
 
 
 
 
 
 1. Big Data and Cloud Mastery
 
 
 
 
 <ul>
 <li>
 
 Programming &amp; Big Data:
 
 10+ years of extensive, hands-on experience with
 
 Apache Spark
 
 , with strong preference for production development using
 
 Scala
 
 . Deep expertise with
 
 Apache Hive
 
 for data querying and management.
 </li>
 <li>
 
 Cloud Proficiency:
 
 Demonstrated expertise in deploying and managing data/ML workloads across
 
 at least two
 
 of the three major cloud platforms:
 
 AWS
 
 (Sagemaker, EMR, S3),
 
 Azure
 
 (Azure ML, Synapse Analytics), and
 
 GCP
 
 (Vertex AI, BigQuery).
 </li>
 <li>
 
 Data Architecture:
 
 Expert knowledge of distributed systems, data partitioning, optimization techniques, and data warehousing concepts in a cloud-native context.
 </li>
 </ul>
 
 
 
 
 2. Advanced AI/ML Specialization
 
 
 
 
 <ul>
 <li>
 
 Generative AI (GenAI) &amp; LLMs:
 
 Proven experience with the architecture and implementation of Generative AI solutions, including prompt engineering, fine-tuning, and deploying
 
 Large Language Models (LLMs)
 
 .
 </li>
 <li>
 
 Computer Vision:
 
 In-depth knowledge of deep learning frameworks (TensorFlow, PyTorch) and experience with Computer Vision tasks (e.g., CNNs, object detection models like YOLO).
 </li>
 <li>
 
 NLP:
 
 Expert practical experience in NLP techniques, including transformer models, embedding generation, and building complex text-based applications.
 </li>
 </ul>
 
 
 
 
 3. Leadership &amp; Soft Skills
 
 
 
 
 <ul>
 <li>
 
 Technical Leadership:
 
 Proven track record of leading complex data science projects from research to production deployment.
 </li>
 <li>
 
 Communication:
 
 Exceptional ability to translate complex technical findings into clear, strategic recommendations for technical and executive audiences.
 </li>
 <li>
 
 Mentorship:
 
 Experience mentoring and training senior engineers and data scientists.
 </li>
 </ul>
 
 
 
 
 Education and Certification
 
 
 
 
 <ul>
 <li>
 Master&rsquo;s or Ph.D. in Computer Science, Data Science, Engineering, or a highly quantitative field.
 </li>
 <li>
 Relevant professional cloud certifications (e.g., AWS Certified Machine Learning Specialty, Google Cloud Professional Data Engineer) are highly desirable.
 </li>
 </ul>
 
 
</div>

Senior Data Scientist SME & AI

SME Careers

<div>
 As a remote, hourly paid R Engineer, you will review AI-generated responses and generate high-quality R and data-analysis-focused content, evaluating the reasoning quality and step-by-step problem-solving behind each solution. You will assess solutions for accuracy, clarity, and adherence to the prompt; identify errors in statistical methodology or data-wrangling workflows; fact-check analytical results; write expert-level explanations and model solutions that demonstrate correct use of R; and rate and compare multiple AI responses based on correctness and reasoning quality. This contract role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate that provides AI training data for many of the world&rsquo;s largest AI companies and foundation model labs, directly helping to improve the world&rsquo;s premier AI models.
 
 
 Key Responsibilities
 
 
 <ul>
 <li>
 Develop AI Training Content: Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects.
 </li>
 <li>
 Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance.
 </li>
 <li>
 Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases.
 
 </li>
 </ul>
 
 Your Profile
 
 
 <ul>
 <li>
 2+ years of hands-on experience using R for data analysis, statistics, or data science work.
 </li>
 <li>
 Strong proficiency in R programming, including data wrangling, functional programming patterns, and writing reusable functions or packages.
 </li>
 <li>
 Solid grounding in applied statistics, including regression, inference, and model validation, with practical experience implementing these methods in R.
 </li>
 <li>
 Experience building end-to-end analyses in R that include data cleaning, exploratory analysis, modeling, and visualization.
 </li>
 <li>
 Familiarity with common R ecosystems such as tidyverse, data.table, and ggplot2, and the ability to choose appropriate tools for a given task.
 </li>
 <li>
 Professional experience in a data-focused role such as data scientist, statistician, quantitative analyst, or similar.
 </li>
 <li>
 Minimum Bachelor&rsquo;s degree in Statistics, Mathematics, Computer Science, or a closely related quantitative field.
 </li>
 <li>
 Significant experience using large language models (LLMs) to assist with coding, analysis design, and code review in R.
 </li>
 <li>
 Excellent English writing skills with the ability to document analyses and explain complex statistical ideas clearly to non-experts.
 </li>
 <li>
 Previous experience with AI data training or model evaluation is strongly preferred, and Minimum C1 English proficiency is required.
 </li>
 </ul>
</div>

R Engineer

<div><h2 class="jobSectionHeader">Job Title: Data Engineer (2 Positions)</h2>
Experience: 6&ndash;7 Years 
Location: Remote / Hybrid 
Employment Type: Full-Time<h3 class="jobSectionHeader">
Job Summary</h3>
We are seeking talented and experienced Data Engineers with 6&ndash;7 years of hands-on experience in designing, developing, and maintaining large-scale data solutions. The ideal candidates will have a strong background in building robust ETL frameworks, complex data pipelines, and working with big data technologies. This role requires a blend of technical expertise, problem-solving skills, and the ability to work collaboratively with cross-functional teams to deliver reliable and scalable data systems supporting analytics and business intelligence.<h3 class="jobSectionHeader">
Key Responsibilities</h3><ul><li>
Design, develop, and maintain scalable ETL/ELT frameworks and complex data pipelines.</li><li>
Build and optimize data processing jobs using big data technologies and modern data frameworks.</li><li>
Ensure data accuracy, consistency, and reliability across multiple data platforms.</li><li>
Collaborate with data scientists, analysts, and business stakeholders to transform business requirements into data-driven technical solutions.</li><li>
Troubleshoot, debug, and optimize existing workflows for performance and scalability.</li><li>
Implement best practices for data governance, version control, and performance monitoring.</li></ul><h3 class="jobSectionHeader">
Required Skills &amp; Qualifications</h3><ul><li>
6&ndash;7 years of hands-on experience in data engineering and ETL design.</li><li>
Proficiency in SQL and strong experience with one or more ETL tools such as Talend, Informatica, or dbt.</li><li>
Expertise in big data technologies including Apache Spark, Hadoop, and Kafka.</li><li>
Solid programming skills in Python or Scala.</li><li>
Strong understanding of data modeling, data integration, and data quality management.</li><li>
Experience with cloud data platforms (AWS, Azure, or GCP) is a strong plus.</li><li>
Excellent problem-solving, analytical, and communication skills.</li></ul><h3 class="jobSectionHeader">
Preferred Qualifications</h3><ul><li>
Experience working in agile data environments.</li><li>
Exposure to data warehousing or data lake architectures.</li><li>
Familiarity with DevOps practices and CI/CD for data pipelines.</li></ul>
lhr1NSVLnW</div>

Data Engineer

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