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B1 – Data Scientist - Kofax Accredited Developers
Requirement – 3
Mandatory –
- 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
Qualification -
- Masters in Statistics/Mathematics/Economics/Econometrics Or BE/B-Tech, MCA or MBA
Understand business problems and translate business requirements into technical requirements.
Conduct complex data analysis to ensure data quality & reliability i.e., make the data talk by extracting, preparing, and transforming it.
Identify, develop and implement statistical techniques and algorithms to address business challenges and add value to the organization.
Gather requirements and communicate findings in the form of a meaningful story with the stakeholders.
Build & implement data models using predictive modelling techniques. Interact with clients and provide support for queries and delivery
adoption.
Lead and mentor data analysts.
What we are looking for-
Apart from your love for data and ability to code even while sleeping you would need the following.
Minimum of 02 years of experience in designing and delivery of data science solutions.
You should have successful projects of retail/BFSI/FMCG/Manufacturing/QSR in your kitty to show-off.
Deep understanding of various statistical techniques, mathematical models, and algorithms to start the conversation with the data in hand.
Ability to choose the right model for the data and translate that into a code using R, Python, VBA, SQL, etc.
Bachelors/Masters degree in Engineering/Technology or MBA from
Tier-1 B School or MSc. in Statistics or Mathematics.
- Partnering with internal business owners (product, marketing, edit, etc.) to understand needs and develop custom analysis to optimize for user engagement and retention
- Good understanding of the underlying business and workings of cross functional teams for successful execution
- Design and develop analyses based on business requirement needs and challenges.
- Leveraging statistical analysis on consumer research and data mining projects, including segmentation, clustering, factor analysis, multivariate regression, predictive modeling, etc.
- Providing statistical analysis on custom research projects and consult on A/B testing and other statistical analysis as needed. Other reports and custom analysis as required.
- Identify and use appropriate investigative and analytical technologies to interpret and verify results.
- Apply and learn a wide variety of tools and languages to achieve results
- Use best practices to develop statistical and/ or machine learning techniques to build models that address business needs.
Requirements
- 2 - 4 years of relevant experience in Data science.
- Preferred education: Bachelor's degree in a technical field or equivalent experience.
- Experience in advanced analytics, model building, statistical modeling, optimization, and machine learning algorithms.
- Machine Learning Algorithms: Crystal clear understanding, coding, implementation, error analysis, model tuning knowledge on Linear Regression, Logistic Regression, SVM, shallow Neural Networks, clustering, Decision Trees, Random forest, XGBoost, Recommender Systems, ARIMA and Anomaly Detection. Feature selection, hyper parameters tuning, model selection and error analysis, boosting and ensemble methods.
- Strong with programming languages like Python and data processing using SQL or equivalent and ability to experiment with newer open source tools.
- Experience in normalizing data to ensure it is homogeneous and consistently formatted to enable sorting, query and analysis.
- Experience designing, developing, implementing and maintaining a database and programs to manage data analysis efforts.
- Experience with big data and cloud computing viz. Spark, Hadoop (MapReduce, PIG, HIVE).
- Experience in risk and credit score domains preferred.
Senior Data Scientist-Job Description
The Senior Data Scientist role is a creative problem solver who utilizes statistical/mathematical principles and modelling skills to uncover new insights that will significantly and meaningfully impact business decisions and actions. She/he applies their data science expertise in identifying, defining, and executing state-of-art techniques for academic opportunities and business objectives in collaboration with other Analytics team members. The Senior Data Scientist will execute analyses & outputs spanning test design and measurement, predictive analytics, multivariate analysis, data/text mining, pattern recognition, artificial intelligence, and machine learning.
Key Responsibilities:
- Perform the full range of data science activities including test design and measurement, predictive/advanced analytics, and data mining, and analytic dashboards.
- Extract, manipulate, analyse & interpret data from various corporate data sources developing advanced analytic solutions, deriving key observations, findings, insights, and formulating actionable recommendations.
- Generate clearly understood and intuitive data science / advanced analytics outputs.
- Provide thought leadership and recommendations on business process improvement, analytic solutions to complex problems.
- Participate in best practice sharing and communication platform for advancement of the data science discipline.
- Coach and collaborate with other data scientists and data analysts.
- Present impact, insights, outcomes & recommendations to key business partners and stakeholders.
- Comply with established Service Level Agreements to ensure timely, high quality deliverables with value-add recommendations, clearly articulated key findings and observations.
Qualification:
- Bachelor's Degree (B.A./B.S.) or Master’s Degree (M.A./M.S.) in Computer Science, Statistics, Mathematics, Machine Learning, Physics, or similar degree
- 5+ years of experience in data science in a digitally advanced industry focusing on strategic initiatives, marketing and/or operations.
- Advanced knowledge of best-in-class analytic software tools and languages: Python, SQL, R, SAS, Tableau, Excel, PowerPoint.
- Expertise in statistical methods, statistical analysis, data visualization, and data mining techniques.
- Experience in Test design, Design of Experiments, A/B Testing, Measurement Science Strong influencing skills to drive a robust testing agenda and data driven decision making for process improvements
- Strong Critical thinking skills to track down complex data and engineering issues, evaluate different algorithmic approaches, and analyse data to solve problems.
- Experience in partnering with IT, marketing operations & business operations to deploy predictive analytic solutions.
- Ability to translate/communicate complex analytical/statistical/mathematical concepts with non-technical audience.
- Strong written and verbal communications skills, as well as presentation skills.
Key deliverables for the Data Science Engineer would be to help us discover the information hidden in vast amounts of data, and help us make smarter decisions to deliver even better products. Your primary focus will be on applying data mining techniques, doing statistical analysis, and building high-quality prediction systems integrated with our products.
What will you do?
- You will be building and deploying ML models to solve specific business problems related to NLP, computer vision, and fraud detection.
- You will be constantly assessing and improving the model using techniques like Transfer learning
- You will identify valuable data sources and automate collection processes along with undertaking pre-processing of structured and unstructured data
- You will own the complete ML pipeline - data gathering/labeling, cleaning, storage, modeling, training/testing, and deployment.
- Assessing the effectiveness and accuracy of new data sources and data gathering techniques.
- Building predictive models and machine-learning algorithms to apply to data sets.
- Coordinate with different functional teams to implement models and monitor outcomes.
- Presenting information using data visualization techniques and proposing solutions and strategies to business challenges
We would love to hear from you if :
- You have 2+ years of experience as a software engineer at a SaaS or technology company
- Demonstrable hands-on programming experience with Python/R Data Science Stack
- Ability to design and implement workflows of Linear and Logistic Regression, Ensemble Models (Random Forest, Boosting) using R/Python
- Familiarity with Big Data Platforms (Databricks, Hadoop, Hive), AWS Services (AWS, Sagemaker, IAM, S3, Lambda Functions, Redshift, Elasticsearch)
- Experience in Probability and Statistics, ability to use ideas of Data Distributions, Hypothesis Testing and other Statistical Tests.
- Demonstrable competency in Data Visualisation using the Python/R Data Science Stack.
- Preferable Experience Experienced in web crawling and data scraping
- Strong experience in NLP. Worked on libraries such as NLTK, Spacy, Pattern, Gensim etc.
- Experience with text mining, pattern matching and fuzzy matching
Why Tartan?
- Brand new Macbook
- Stock Options
- Health Insurance
- Unlimited Sick Leaves
- Passion Fund (Invest in yourself or your passion project)
- Wind Down
1. Ability to work independently and to set priorities while managing several projects simultaneously; strong attention to detail is essential.
2.Collaborates with Business Systems Analysts and/or directly with key business users to ensure business requirements and report specifications are documented accurately and completely.
3.Develop data field mapping documentation.
4. Document data sources and processing flow.
5. Ability to design, refine and enhance existing reports from source systems or data warehouse.
6.Ability to analyze and optimize data including data deduplication required for reports.
7. Analysis and rationalization of reports.
8. Support QA and UAT teams in defining test scenarios and clarifying requirements.
9. Effectively communicate results of the data analysis to internal and external customers to support decision making.
10.Follows established SDLC, change control, release management and incident management processes.
11.Perform source data analysis and assessment.
12. Perform data profiling to capture business and technical rules.
13. Track and help to remediate issues and defects due to data quality exceptions.
Work shift: Day time
- Strong problem-solving skills with an emphasis on product development.
insights from large data sets.
• Experience in building ML pipelines with Apache Spark, Python
• Proficiency in implementing end to end Data Science Life cycle
• Experience in Model fine-tuning and advanced grid search techniques
• Experience working with and creating data architectures.
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural
networks, etc.) and their real-world advantages/drawbacks.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions,
statistical tests and proper usage, etc.) and experience with applications.
• Excellent written and verbal communication skills for coordinating across teams.
• A drive to learn and master new technologies and techniques.
• Assess the effectiveness and accuracy of new data sources and data gathering techniques.
• Develop custom data models and algorithms to apply to data sets.
• Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting, and other business outcomes.
• Develop company A/B testing framework and test model quality.
• Coordinate with different functional teams to implement models and monitor outcomes.
• Develop processes and tools to monitor and analyze model performance and data accuracy.
Key skills:
● Strong knowledge in Data Science pipelines with Python
● Object-oriented programming
● A/B testing framework and model fine-tuning
● Proficiency in using sci-kit, NumPy, and pandas package in python
Nice to have:
● Ability to work with containerized solutions: Docker/Compose/Swarm/Kubernetes
● Unit testing, Test-driven development practice
● DevOps, Continuous integration/ continuous deployment experience
● Agile development environment experience, familiarity with SCRUM
● Deep learning knowledge
culture and operating norms as a result of the fast-paced nature of a new, high-growth
organization.
• 7+ years of Industry experience primarily related to Unstructured Text Data and NLP
(PhD work and internships will be considered if they are related to unstructured text
in lieu of industry experience but not more than 2 years will be accounted towards
industry experience)
• Develop Natural Language Medical/Healthcare documents comprehension related
products to support Health business objectives, products and improve
processing efficiency, reducing overall healthcare costs
• Gather external data sets; build synthetic data and label data sets as per the needs
for NLP/NLR/NLU
• Apply expert software engineering skills to build Natural Language products to
improve automation and improve user experiences leveraging unstructured data storage, Entity Recognition, POS Tagging, ontologies, taxonomies, data mining,
information retrieval techniques, machine learning approach, distributed and cloud
computing platforms
• Own the Natural Language and Text Mining products — from platforms to systems
for model training, versioning, deploying, storage and testing models with creating
real time feedback loops to fully automated services
• Work closely and collaborate with Data Scientists, Machine Learning engineers, IT
teams and Business stakeholders spread out across various locations in US and India
to achieve business goals
• Provide mentoring to other Data Scientist and Machine Learning Engineers
• Strong understanding of mathematical concepts including but not limited to linear
algebra, Advanced calculus, partial differential equations and statistics including
Bayesian approaches
• Strong programming experience including understanding of concepts in data
structures, algorithms, compression techniques, high performance computing,
distributed computing, and various computer architecture
• Good understanding and experience with traditional data science approaches like
sampling techniques, feature engineering, classification and regressions, SVM, trees,
model evaluations
• Additional course work, projects, research participation and/or publications in
Natural Language processing, reasoning and understanding, information retrieval,
text mining, search, computational linguistics, ontologies, semantics
• Experience with developing and deploying products in production with experience
in two or more of the following languages (Python, C++, Java, Scala)
• Strong Unix/Linux background and experience with at least one of the following
cloud vendors like AWS, Azure, and Google for 2+ years
• Hands on experience with one or more of high-performance computing and
distributed computing like Spark, Dask, Hadoop, CUDA distributed GPU (2+ years)
• Thorough understanding of deep learning architectures and hands on experience
with one or more frameworks like tensorflow, pytorch, keras (2+ years)
• Hands on experience with libraries and tools like Spacy, NLTK, Stanford core NLP,
Genism, johnsnowlabs for 5+ years
• Understanding business use cases and be able to translate them to team with a
vision on how to implement
• Identify enhancements and build best practices that can help to improve the
productivity of the team.
- B.Tech/MTech from tier 1 institution
- 8+years of experience in machine learning techniques like logistic regression, random forest, boosting, trees, neural networks, etc.
- Showcased experience with Python, SQL and proficiency in Scikit Learn, Pandas, NumPy, Keras and TensorFlow/pytorch
- Experience of working with Qlik sense or Tableau is a plus