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Data Scientist

Requirements

● B.Tech/Masters in Mathematics, Statistics, Computer Science or another
quantitative field
● 2-3+ years of work experience in ML domain ( 2-5 years experience )
● Hands-on coding experience in Python
● Experience in machine learning techniques such as Regression, Classification,
Predictive modeling, Clustering, Deep Learning stack, NLP
● Working knowledge of Tensorflow/PyTorch

Optional Add-ons-

● Experience with distributed computing frameworks: Map/Reduce, Hadoop, Spark
etc.
● Experience with databases: MongoDB

Job Responsibilities

• Design machine learning systems
• Research and implement appropriate ML algorithms and tools
• Develop machine learning applications according to requirements
• Select appropriate datasets and data representation methods
• Run machine learning tests and experiments
• Perform statistical analysis and fine-tuning using test results
• Train and retrain systems when necessary

 

Requirements for the Job

 

1. Bachelor’s/Master's/PhD in Computer Science, Mathematics, Statistics or equivalent field andmust have a minimum of 2 years of overall experience in tier one colleges 

1. Minimum 1 year of experience working as a Data Scientist in deploying ML at scale in production
2. Experience in machine learning techniques (e.g. NLP, Computer Vision, BERT, LSTM etc..) andframeworks (e.g. TensorFlow, PyTorch, Scikit-learn, etc.)

1. Working knowledge in deployment of Python systems (using Flask, Tensorflow Serving)
2. Previous experience in following areas will be preferred: Natural Language Processing(NLP) - Using LSTM and BERT; chatbots or dialogue systems, machine translation, comprehension of text, text summarization.
3. Computer Vision - Deep Neural Networks/CNNs for object detection and image classification, transfer learning pipeline and object detection/instance segmentation (Mask R-CNN, Yolo, SSD).

Position Summary:
You will be responsible for
1. Setting up, maintaining cloud (AWS/GCP/Azure) and kubernetes cluster and automating
their operation
2. All operational aspects of devtron platform including maintenance, upgrades,
automation.
3. Providing kubernetes expertise to facilitate smooth and fast customer onboarding on
devtron platform
Responsibilities:
1. Manage devtron platform on multiple kubernetes clusters
2. Designing and embedding industry best practices for online services including disaster
recovery, business continuity, monitoring/alerting, and service health measurement
3. Providing operational support for day to day activities involving the deployment of
services
4. Identify opportunities for improving the security, reliability, and scalability of the platform
5. Facilitate smooth and fast customer onboarding on devtron platform
6. Drive customer engagement
Requirements:
● Bachelor's Degree in Computer Science or a related field.
● 2+ years working as a devops engineer
● Proficient in 1 or more programming languages (e.g. Python, Go, Ruby).
● Familiar with shell scripts, Linux commands, network fundamentals
● Understanding of large scale distributed systems
● Basic understanding of cloud computing (AWS/GCP/Azure)
Preferred Qualifications:
● Great analytical and interpersonal skills
● Passion for creating efficient, reliable, reusable programs/scripts.
● Excited about technology, have a strong interest in learning about and playing with the
latest technologies and doing POC.
● Strong customer focus, ownership, urgency and drive.
● Knowledge and experience with cloud native tools like prometheus, kubernetes, docker,
grafana.

Data analyst responsibilities include conducting full lifecycle analysis to include requirements, activities and design. Data analysts will develop analysis and reporting capabilities. They will also monitor performance and quality control plans to identify improvements.

• Interpret data, analyze results using statistical techniques and provide ongoing reports
• Develop and implement databases, data collection systems, data analytics and other strategies that optimize statistical efficiency and quality
• Acquire data from primary or secondary data sources and maintain databases/data systems
• Identify, analyze, and interpret trends or patterns in complex data sets
• Filter and “clean” data by reviewing computer reports, printouts, and performance indicators to locate and correct code problems
• Work with management to prioritize business and information needs
• Locate and define new process improvement opportunities
  
  

  Requirements

  • Proven working experience as a Data Analyst or Business Data Analyst
  • https://resources.workable.com/data-scientist-analysis-interview-questions">Technical expertise regarding data models, database design development, data mining and segmentation techniques
  • Strong knowledge of and experience with reporting packages (Business Objects etc), databases (SQL etc), programming (XML, Javascript, or ETL frameworks)
  • Knowledge of statistics and experience using statistical packages for analyzing datasets (Excel, SPSS, SAS etc)
  • Strong https://resources.workable.com/analytical-skills-interview-questions">analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
  • Adept at queries, report writing and presenting findings
  • BS in Mathematics, Economics, Computer Science, Information Management or Statistics

Job Description:

• Help build a Data Science team which will be engaged in researching, designing,
implementing, and deploying full-stack scalable data analytics vision and machine learning
solutions to challenge various business issues.
• Modelling complex algorithms, discovering insights and identifying business
opportunities through the use of algorithmic, statistical, visualization, and mining techniques
• Translates business requirements into quick prototypes and enable the
development of big data capabilities driving business outcomes
• Responsible for data governance and defining data collection and collation
guidelines.
• Must be able to advice, guide and train other junior data engineers in their job.

Must Have:

• 4+ experience in a leadership role as a Data Scientist
• Preferably from retail, Manufacturing, Healthcare industry(not mandatory)
• Willing to work from scratch and build up a team of Data Scientists
• Open for taking up the challenges with end to end ownership
• Confident with excellent communication skills along with a good decision maker

Job Description:

• Help build a Data Science team which will be engaged in researching, designing,
implementing, and deploying full-stack scalable data analytics vision and machine learning
solutions to challenge various business issues.
• Modelling complex algorithms, discovering insights and identifying business
opportunities through the use of algorithmic, statistical, visualization, and mining techniques
• Translates business requirements into quick prototypes and enable the
development of big data capabilities driving business outcomes
• Responsible for data governance and defining data collection and collation
guidelines.
• Must be able to advice, guide and train other junior data engineers in their job.

Must Have:

• 4+ experience in a leadership role as a Data Scientist
• Preferably from retail, Manufacturing, Healthcare industry(not mandatory)
• Willing to work from scratch and build up a team of Data Scientists
• Open for taking up the challenges with end to end ownership
• Confident with excellent communication skills along with a good decision maker

What you will be doing:

As a part of the Global Credit Risk and Data Analytics team, this person will be responsible for carrying out analytical initiatives which will be as follows: -

• Dive into the data and identify patterns
• Development of end-to-end Credit models and credit policy for our existing credit products
• Leverage alternate data to develop best-in-class underwriting models
• Working on Big Data to develop risk analytical solutions
• Development of Fraud models and fraud rule engine
• Collaborate with various stakeholders (e.g. tech, product) to understand and design best solutions which can be implemented
• Working on cutting-edge techniques e.g. machine learning and deep learning models

Example of projects done in past:

• Lazypay Credit Risk model using CatBoost modelling technique ; end-to-end pipeline for feature engineering and model deployment in production using Python
• Fraud model development, deployment and rules for EMEA region

 

Basic Requirements:

• 1-3 years of work experience as a Data scientist (in Credit domain)
• 2016 or 2017 batch from a premium college (e.g B.Tech. from IITs, NITs, Economics from DSE/ISI etc)
• Strong problem solving and understand and execute complex analysis
• Experience in at least one of the languages - R/Python/SAS and SQL
• Experience in in Credit industry (Fintech/bank)
• Familiarity with the best practices of Data Science

 

Add-on Skills : 

• Experience in working with big data
• Solid coding practices
• Passion for building new tools/algorithms
• Experience in developing Machine Learning models

What you will do:
As a Data Science Lead, you will be working on creating industry first analytical and propensity models to
help discover the information hidden in vast amounts of data, and make smarter decisions to deliver
even better customer experience. Your primary focus will be in applying data mining techniques, doing
statistical analysis, and building high quality prediction systems integrated with our products.

➢ Working with business and leadership teams to gathering and analyse structured and unstructured data
➢ Data mining using state-of-the-art methods
➢ Enhancing data collection procedures to include information that is relevant for building analytic
systems
➢ Processing, cleansing, and verifying the integrity of data used for analysis
➢ Doing ad-hoc analysis and presenting results in a clear manner
➢ Creating automated anomaly detection systems and constant tracking of its performance
➢ Creation and evolution of an efficient BI pipeline into a multi-faceted pipeline to support various
modelling needs.

What we are looking for:

➢ 5-8 years of relevant experience, preferably in financial services industry.
➢ A bachelors / master’s degree in the field of Statistics, Mathematics, Computer Science or
Management from Tier 1 Institutes.
➢ Data warehousing experience will be a plus.
➢ Good conceptual understanding of statistics and probability.
➢ Experience in developing dashboards and reports using BI tools.

• Act as a lead analyst on various data analytics projects aiding strategic decision making for Fortune 500 / FTSE 100 companies, Blue Chip Consulting Firms and Global Financial Services companies • Understand the client objectives, and work with the PL to design the analytical solution/framework. Be able to translate the client objectives / analytical plan into clear deliverables with associated priorities and constraints • Collect/Organize/Prepare/Manage data for the analysis and conduct quality checks • Use and implement basic and advanced statistical techniques like frequencies, cross-tabs, correlation, Regression, Decision Trees, Cluster Analysis, etc. to identify key actionable insights from the data • Develop complete sections of final client report in Power Point. Identify trends and evaluate insights in terms of logic and reasoning, and be able to succinctly present them in terms of an executive summary/taglines • Conduct sanity checks of the analysis output based on reasoning and common sense, and be able to do a rigorous self QC, as well as of the work assigned to analysts to ensure an error free output • Aid in decision making related to client management, and also be able to take client calls relatively independently • Support the project leads in managing small teams of 2-3 analysts, independently set targets and communicate to team members • Discuss queries/certain sections of deliverable report over client calls or video conferences Technical Skills: • Hands on experience of one or more statistical tools such as SAS, R and Python • Working knowledge or experience in using SQL Server (or other RDBMS tools) would be an advantage Work Experience: • 2-4 years of relevant experience in Marketing Analytics / MR. • Experience in managing, cleaning and analysis of large datasets using statistical packages like SAS, R, Python, etc. • Experience in data management using SQL queries on tools like Access/ SQL Server

• Using statistical and machine learning techniques to analyse large-scale user data, including text data and chat logs; • Applying machine learning techniques for text mining and information extraction based on structured, semi-structured and unstructured data; • Contributing to services like chatbots, voice portals and dialogue systems • Input your own ideas to improve existing processes on services and products

Job Description – Data Scientist About Company Profile Precily is a startup headquartered in Noida, IN. Precily is currently working with leading consulting & law firms, research firms & technology companies. Aura (Precily AI) is data-analysis platform for enterprises that increase the efficiency of the workforce by providing AI-based solutions. Responsibilities & Skills Required: The role requires deep knowledge in designing, planning, testing and deploying analytics solutions including the following: • Natural Language Processing (NLP), Neural Networks , Text Clustering, Topic Modelling, Information Extraction, Information Retrieval, Deep learning, Machine learning, cognitive science and analytics. • Proven experience implementing and deploying advanced AI solutions using R/Python. • Apply machine learning algorithms, statistical data analysis, text clustering, summarization, extracting insights from multiple data points. • Excellent understanding of Analytics concepts and methodologies including machine learning (unsupervised and supervised). • Hand on in handling large amounts of structured and unstructured data. • Measure, interpret, and derive learning from results of analysis that will lead to improvements document processing. Skills Required: • Python, R, NLP, NLG, Machine Learning, Deep Learning & Neural Networks • Word Vectorizers • Word Embeddings ( word2vec & GloVe ) • RNN ( CNN vs RNN ) • LSTM & GRU ( LSTM vs GRU ) • Pretrained Embeddings ( Implementation in RNN ) • Unsupervised Learning • Supervised Learning • Deep Neural Networks • Framework : Keras/tensorflow • Keras Embedding Layer output Please reach out to us: [email protected]