Data Scientist (Kofax Accredited Developers)
at A global business process management company
B1 – Data Scientist - Kofax Accredited Developers
Requirement – 3
- Accreditation of Kofax KTA / KTM
- Experience in Kofax Total Agility Development – 2-3 years minimum
- Ability to develop and translate functional requirements to design
- Experience in requirement gathering, analysis, development, testing, documentation, version control, SDLC, Implementation and process orchestration
- Experience in Kofax Customization, writing Custom Workflow Agents, Custom Modules, Release Scripts
- Application development using Kofax and KTM modules
- Good/Advance understanding of Machine Learning /NLP/ Statistics
- Exposure to or understanding of RPA/OCR/Cognitive Capture tools like Appian/UI Path/Automation Anywhere etc
- Excellent communication skills and collaborative attitude
- Work with multiple teams and stakeholders within like Analytics, RPA, Technology and Project management teams
- Good understanding of compliance, data governance and risk control processes
Total Experience – 7-10 Years in BPO/KPO/ ITES/BFSI/Retail/Travel/Utilities/Service Industry
Good to have
- Previous experience of working on Agile & Hybrid delivery environment
- Knowledge of VB.Net, C#( C-Sharp ), SQL Server , Web services
- Masters in Statistics/Mathematics/Economics/Econometrics Or BE/B-Tech, MCA or MBA
Skills: Machine Learning,Deep Learning,Artificial Intelligence,python.
Domain knowledge: Data cleaning, modelling, analytics, statistics, machine learning, AI
· To be part of Digital Manufacturing and Industrie 4.0 projects across Saint Gobain group of companies
· Design and develop AI//ML models to be deployed across SG factories
· Knowledge on Hadoop, Apache Spark, MapReduce, Scala, Python programming, SQL and NoSQL databases is required
· Should be strong in statistics, data analysis, data modelling, machine learning techniques and Neural Networks
· Prior experience in developing AI and ML models is required
· Experience with data from the Manufacturing Industry would be a plus
Roles and Responsibilities:
· Develop AI and ML models for the Manufacturing Industry with a focus on Energy, Asset Performance Optimization and Logistics
· Multitasking, good communication necessary
· Entrepreneurial attitude.
Purpose of Job:
Responsible to lead a team of analysts to build and deploy predictive models to infuse core
business functions with deep analytical insights. The Senior Data Scientist will also work
closely with the Kinara management team to investigate strategically important business questions.
Lead a team through the entire analytical and machine learning model life cycle:
Define the problem statement
Build and clean datasets
Exploratory data analysis
Apply ML algorithms and assess the performance
Code for deployment
Code testing and troubleshooting
Communicate Analysis to Stakeholders
Manage Data Analysts and Data Scientists
Education: MS/MTech/Btech graduates or equivalent with a focus on data science and
quantitative fields (CS, Engineering, Mathematics, Economics)
Work Experience: 5+ years in a professional role with 3+ years in ML/AI
Other Requirements: ⮚ Domain knowledge in Financial Services is a big plus
Skills & Competencies
⮚ Aptitude in Math and Stats
⮚ Proven experience in the use of Python, SQL, DevOps
⮚ Excellent in programming (Python), stats tools, and SQL
⮚ Working knowledge of tools and utilities - AWS, Git, Selenium, Postman,Prefect, Airflow, PySpark
⮚ Deep Curiosity and Humility
⮚ Strong communications verbal and written
Job Description – Data Science
- ME/MS from premier institute with a background in Mechanical/Industrial/Chemical/Materials engineering.
- Strong Analytical skills and application of Statistical techniques to problem solving
- Expertise in algorithms, data structures and performance optimization techniques
- Proven track record of demonstrating end to end ownership involving taking an idea from incubator to market
- Minimum years of experience in data analysis (2+), statistical analysis, data mining, algorithms for optimization.
The Data Engineer/Analyst will
- Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
- Clear interaction with Business teams including product planning, sales, marketing, finance for defining the projects, objectives.
- Mine and analyze data from company databases to drive optimization and improvement of product and process development, marketing techniques and business strategies
- Coordinate with different R&D and Business teams to implement models and monitor outcomes.
- Mentor team members towards developing quick solutions for business impact.
- Skilled at all stages of the analysis process including defining key business questions, recommending measures, data sources, methodology and study design, dataset creation, analysis execution, interpretation and presentation and publication of results.
- 4+ years’ experience in MNC environment with projects involving ML, DL and/or DS
- Experience in Machine Learning, Data Mining or Machine Intelligence (Artificial Intelligence)
- Knowledge on Microsoft Azure will be desired.
- Expertise in machine learning such as Classification, Data/Text Mining, NLP, Image Processing, Decision Trees, Random Forest, Neural Networks, Deep Learning Algorithms
- Proficient in Python and its various libraries such as Numpy, MatPlotLib, Pandas
- Superior verbal and written communication skills, ability to convey rigorous mathematical concepts and considerations to Business Teams.
- Experience in infra development / building platforms is highly desired.
- A drive to learn and master new technologies and techniques.
We are looking for an experienced engineer to join our data science team, who will help us design, develop, and deploy machine learning models in production. You will develop robust models, prepare their deployment into production in a controlled manner, while providing appropriate means to monitor their performance and stability after deployment.
What You’ll Do will include (But not limited to):
- Preparing datasets needed to train and validate our machine learning models
- Anticipate and build solutions for problems that interrupt availability, performance, and stability in our systems, services, and products at scale.
- Defining and implementing metrics to evaluate the performance of the models, both for computing performance (such as CPU & memory usage) and for ML performance (such as precision, recall, and F1)
- Supporting the deployment of machine learning models on our infrastructure, including containerization, instrumentation, and versioning
- Supporting the whole lifecycle of our machine learning models, including gathering data for retraining, A/B testing, and redeployments
- Developing, testing, and evaluating tools for machine learning models deployment, monitoring, retraining.
- Working closely within a distributed team to analyze and apply innovative solutions over billions of documents
- Supporting solutions ranging from rule-bases, classical ML techniques to the latest deep learning systems.
- Partnering with cross-functional team members to bring large scale data engineering solutions to production
- Communicating your approach and results to a wider audience through presentations
- Demonstrated success with machine learning in a SaaS or Cloud environment, with hands–on knowledge of model creation and deployments in production at scale
- Good knowledge of traditional machine learning methods and neural networks
- Experience with practical machine learning modeling, especially on time-series forecasting, analysis, and causal inference.
- Experience with data mining algorithms and statistical modeling techniques for anomaly detection in time series such as clustering, classification, ARIMA, and decision trees is preferred.
- Ability to implement data import, cleansing and transformation functions at scale
- Fluency in Docker, Kubernetes
- Working knowledge of relational and dimensional data models with appropriate visualization techniques such as PCA.
- Solid English skills to effectively communicate with other team members
Due to the nature of the role, it would be nice if you have also:
- Experience with large datasets and distributed computing, especially with the Google Cloud Platform
- Fluency in at least one deep learning framework: PyTorch, TensorFlow / Keras
- Experience with No–SQL and Graph databases
- Experience working in a Colab, Jupyter, or Python notebook environment
- Some experience with monitoring, analysis, and alerting tools like New Relic, Prometheus, and the ELK stack
- Knowledge of Java, Scala or Go-Lang programming languages
- Familiarity with KubeFlow
- Experience with transformers, for example the Hugging Face libraries
- Experience with OpenCV
In a content critical age, Egnyte fuels business growth by enabling content-rich business processes, while also providing organizations with visibility and control over their content assets. Egnyte’s cloud-native content services platform leverages the industry’s leading content intelligence engine to deliver a simple, secure, and vendor-neutral foundation for managing enterprise content across business applications and storage repositories. More than 16,000 customers trust Egnyte to enhance employee productivity, automate data management, and reduce file-sharing cost and complexity. Investors include Google Ventures, Kleiner Perkins, Caufield & Byers, and Goldman Sachs. For more information, visit www.egnyte.com
We are looking for a highly capable machine learning engineer to optimize our deep learning systems. You will be evaluating existing deep learning (DL) processes, do hyperparameter tuning, performing statistical analysis (logging and evaluating model’s performance) to resolve data set problems, and enhancing the accuracy of our AI software's predictive automation capabilities.
You will be working with technologies like AWS Sagemaker, TensorFlow JS, TensorFlow/ Keras/TensorBoard to create Deep Learning backends that powers our application.
To ensure success as a machine learning engineer, you should demonstrate solid data science knowledge and experience in Deep Learning role. A first-class machine learning engineer will be someone whose expertise translates into the enhanced performance of predictive automation software. To do this job successfully, you need exceptional skills in DL and programming.
Consulting with managers to determine and refine machine learning objectives.
Designing deep learning systems and self-running artificial intelligence (AI) software to
automate predictive models.
Transforming data science prototypes and applying appropriate ML algorithms and
Carry out data engineering subtasks such as defining data requirements, collecting,
labeling, inspecting, cleaning, augmenting, and moving data.
Carry out modeling subtasks such as training deep learning models, defining
evaluation metrics, searching hyperparameters, and reading research papers.
Carry out deployment subtasks such as converting prototyped code into production
code, working in-depth with AWS services to set up cloud environment for training,
improving response times and saving bandwidth.
Ensuring that algorithms generate robust and accurate results.
Running tests, performing analysis, and interpreting test results.
Documenting machine learning processes.
Keeping abreast of developments in machine learning.
Proven experience as a Machine Learning Engineer or similar role.
Should have indepth knowledge of AWS Sagemaker and related services (like S3).
Extensive knowledge of ML frameworks, libraries, algorithms, data structures, data
modeling, software architecture, and math & statistics.
Experience with Git and Github.
Superb analytical and problem-solving abilities.
Excellent troubleshooting skills.
Good project management skills.
Great communication and collaboration skills.
Excellent time management and organizational abilities.
Bachelor's degree in computer science, data science, mathematics, or a related field;
Master’s degree is a plus.
Work Location : Chennai
Experience Level : 5+yrs
Package : Upto 18 LPA
Notice Period : Immediate Joiners
It's a full-time opportunity with our client.
Mandatory Skills:Machine Learning,Python,Tableau & SQL
--2+ years of industry experience in predictive modeling, data science, and Analysis.
--Experience with ML models including but not limited to Regression, Random Forests, XGBoost.
--Experience in an ML engineer or data scientist role building and deploying ML models or hands on experience developing deep learning models.
--Experience writing code in Python and SQL with documentation for reproducibility.
--Strong Proficiency in Tableau.
--Experience handling big datasets, diving into data to discover hidden patterns, using data visualization tools, writing SQL.
--Experience writing and speaking about technical concepts to business, technical, and lay audiences and giving data-driven presentations.
--AWS Sagemaker experience is a plus not required.
- 6+ months of proven experience as a Data Scientist or Data Analyst
- Understanding of machine-learning and operations research
- Extensive knowledge of R, SQL and Excel
- Analytical mind and business acumen
- Strong Statistical understanding
- Problem-solving aptitude
- BSc/BA in Computer Science, Engineering or relevant field; graduate degree in Data Science or other quantitative field is preferred