Data Scientist - R01572016

Brillio 2
Pune +1 more
On-site

Who this role is best for

Candidates with advanced statistical and machine learning expertise will find this role in Pune emphasizing robust model development and production deployment. The position appears to target professionals with a blend of technical depth and deployment experience.

Best fit for

  • Candidates with 4-6 years of experience in statistical modeling and machine learning algorithms
    — “With at least 4 years of hands-on experience in advanced data science”
  • Professionals skilled in deploying models via KubeFlow and BentoML in production environments
    — “Streamline model deployment and lifecycle management in production environments using KubeFlow and BentoML”
  • Individuals with strong proficiency in Python, PySpark, and R for data manipulation and analysis
    — “Proficient programming in Python and PySpark for data manipulation and model development”

Things to consider

  • The role may require working in a distributed computing environment with large-scale data processing
    — “Background in large-scale data processing and distributed computing environments”
  • Certifications in data science or machine learning are valued as part of the qualification criteria
    — “Certification in Data Science or Machine Learning from a recognized institution”

How to stand out

  • Highlight experience with time series forecasting techniques like ARIMA and ARIMAX on your resume
    — “Mastery of time series forecasting techniques (exponential smoothing, ARIMA, ARIMAX)”
  • Emphasize your ability to translate complex data findings into actionable business recommendations
    — “Present complex data findings to stakeholders, translating insights into actionable recommendations”
  • Showcase your hands-on work with ML frameworks like TensorFlow and PyTorch in your portfolio
    — “Build, train, and deploy scalable models using ML frameworks such as TensorFlow, PyTorch”
  • Demonstrate your expertise in probabilistic graph models during interviews
    — “Hands-on expertise in probabilistic graph models for complex data relationships”
  • Include examples of data quality checks implemented using Great Expectations or Evidently AI
    — “Implement and validate data quality checks with Great Expectations and Evidently AI”
Pace · Fast PacedCollaboration · MediumAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • actionable business insights
  • scalable models
  • data-driven decision-making
  • model deployment and lifecycle management
  • dataset integrity
Typical background
4-6 years of hands-on experience in advanced data science

Skills & requirements

Required

Advanced Data ScienceStatistical AnalysisMachine LearningPythonPysparkRSASSPSSR StudioML Frameworks (tensorflow, Pytorch, Sci-kit Learn, Cntk, Keras, Mxnet)Time Series ForecastingModel DeploymentData Quality ChecksData VisualizationDistance Metrics

Preferred

Great ExpectationsEvidently AICloud-based Model DeploymentLarge-scale Data ProcessingDistributed ComputingFeature EngineeringModel InterpretabilityCloud-based Data Science Platforms

Stack & domain

Advanced Data ScienceStatistical AnalysisMachine LearningPythonPysparkRSASSPSSR StudioTensorFlowPyTorchSci-kit LearnCNTKKerasMxnetTime Series ForecastingExponential SmoothingArimaArimaxModel DeploymentKubeflowBentomlData Quality ChecksGreat ExpectationsEvidently AIDistance MetricsHammingEuclideanManhattanCloud-based Model DeploymentAWS SagemakerAzure MLGoogle Cloud AI PlatformLarge-scale Data ProcessingDistributed ComputingFeature EngineeringModel InterpretabilityData-driven Decision-makingPresenting Complex Data FindingsTranslating Insights Into Actionable RecommendationsMicrosoft Certified: Azure Data Scientist AssociateTensorflow Developer CertificateData ScienceData Visualization

About the role

Original posting from Brillio 2 via Lever

Data Scientist

Job requirements:

Experience Range: With at least 4 years of hands-on experience in advanced data science, including statistical analysis and machine learning, and up to 6 years in similar roles Key Responsibilities:

  • Design and implement robust statistical models using advanced hypothesis testing, regression, and forecasting techniques to deliver actionable business insights
  • Develop and optimize machine learning algorithms for classification, prediction, and probabilistic graph models utilizing Python, PySpark, and R
  • Conduct comprehensive statistical analysis with SAS, SPSS, and R Studio to support data-driven decision-making
  • Build, train, and deploy scalable models using ML frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet
  • Apply advanced time series forecasting methods, including exponential smoothing, ARIMA, and ARIMAX, to analyze trends and predict outcomes
  • Streamline model deployment and lifecycle management in production environments using KubeFlow and BentoML
  • Implement and validate data quality checks with Great Expectations and Evidently AI to ensure dataset integrity
  • Present complex data findings to stakeholders, translating insights into actionable recommendations that drive business outcomesRequired Skills:
  • Advanced application of hypothesis testing methodologies, including T-Test and Z-Test
  • Expert-level regression analysis (linear and logistic) for predictive modeling
  • Proficient programming in Python and PySpark for data manipulation and model development
  • Extensive experience with statistical analysis using SAS and SPSS
  • Hands-on expertise in probabilistic graph models for complex data relationships
  • Mastery of time series forecasting techniques (exponential smoothing, ARIMA, ARIMAX)
  • Implementation of classification algorithms such as decision trees and support vector machines (SVM)
  • Deep familiarity with ML frameworks: TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet
  • Calculation and application of distance metrics (Hamming, Euclidean, Manhattan)
  • Skilled in R and R Studio for statistical analysis and visualizationPreferred Skills:
  • Practical experience with Great Expectations and Evidently AI for advanced data validation
  • Proficiency in cloud-based model deployment tools such as KubeFlow and BentoML
  • Background in large-scale data processing and distributed computing environments
  • Expertise in feature engineering and model interpretability techniques
  • Familiarity with cloud-based data science platforms such as AWS SageMaker, Azure ML, or Google Cloud AI PlatformDesired Qualifications:
  • Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline
  • Certification in Data Science or Machine Learning from a recognized institution, such as Microsoft Certified: Azure Data Scientist Associate or TensorFlow Developer Certificate

Source: Brillio 2 careers (Lever)

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