PostgreSQL Developer

Job Description:

The data science team is responsible for solving business problems with complex data. Data complexity could be characterized in terms of volume, dimensionality and multiple touchpoints/sources. We understand the data, ask fundamental-first-principle questions, apply our analytical and machine learning skills to solve the problem in the best way possible. 

Our ideal candidate

The role would be a client facing one, hence good communication skills are a must. 

The candidate should have the ability to communicate complex models and analysis in a clear and precise manner. 

The candidate would be responsible for:

• Comprehending business problems properly - what to predict, how to build DV, what value addition he/she is bringing to the client, etc.
• Understanding and analyzing large, complex, multi-dimensional datasets and build features relevant for business
• Understanding the math behind algorithms and choosing one over another
• Understanding approaches like stacking, ensemble and applying them correctly to increase accuracy

Desired technical requirements

• Proficiency with Python and the ability to write production-ready codes. 
• Experience in pyspark, machine learning and deep learning
• Big data experience, e.g. familiarity with Spark, Hadoop, is highly preferred
• Familiarity with SQL or other databases.

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

• Brand new Macbook
• Stock Options
• Health Insurance
• Unlimited Sick Leaves
• Passion Fund (Invest in yourself or your passion project)
• Wind Down

Client  An IT Services Major, hiring for a leading insurance player.

Position: SENIOR CONSULTANT

Job Description:

• Azure admin- senior consultant with HD Insights(Big data)

Skills and Experience

• Microsoft Azure Administrator certification
• Bigdata project experience in Azure HDInsight Stack. big data processing frameworks such as Spark, Hadoop, Hive, Kafka or Hbase.
• Preferred: Insurance or BFSI domain experience
• 5 to 5 years of experience is required.

Preferred Education & Experience:

• Bachelor’s or master’s degree in Computer Engineering, Computer Science, Computer Applications, Mathematics, Statistics or related technical field or equivalent practical experience. Relevant experience of at least 3 years in lieu of above if from a different stream of education.

• Well-versed in and 5+ years of hands-on demonstrable experience with:
  ▪ Data Analysis & Data Modeling
  ▪ Database Design & Implementation
  ▪ Database Performance Tuning & Optimization
  ▪ PL/pgSQL & SQL

• 5+ years of hands-on development experience in Relational Database (PostgreSQL/SQL Server/Oracle).

• 5+ years of hands-on development experience in SQL, PL/PgSQL, including stored procedures, functions, triggers, and views.

• Hands-on experience with demonstrable working experience in Database Design Principles, SQL Query Optimization Techniques, Index Management, Integrity Checks, Statistics, and Isolation levels

• Hands-on experience with demonstrable working experience in Database Read & Write Performance Tuning & Optimization.

• Knowledge and Experience working in Domain Driven Design (DDD) Concepts, Object Oriented Programming System (OOPS) Concepts, Cloud Architecture Concepts, NoSQL Database Concepts are added values

• Knowledge and working experience in Oil & Gas, Financial, & Automotive Domains is a plus

• Hands-on development experience in one or more NoSQL data stores such as Cassandra, HBase, MongoDB, DynamoDB, Elastic Search, Neo4J, etc. a plus.

• Collaborating with a team of like-minded and experienced engineers for Tier 1 customers, you will focus on data engineering on large complex data projects. Your work will have an impact on platforms that handle crores of customers and millions of transactions daily.

• As an engineer, you will use the latest cloud services to design and develop reusable core components and frameworks to modernise data integrations in a cloud first world and own those integrations end to end working closely with business units. You will design and build for efficiency, reliability, security and scalability. As a consultant, you will help drive a data engineering culture and advocate best practices.

We are looking for an outstanding ML Architect (Deployments) with expertise in deploying Machine Learning solutions/models into production and scaling them to serve millions of customers. A candidate with an adaptable and productive working style which fits in a fast-moving environment.

Skills:

- 5+ years deploying Machine Learning pipelines in large enterprise production systems.

- Experience developing end to end ML solutions from business hypothesis to deployment / understanding the entirety of the ML development life cycle.
- Expert in modern software development practices; solid experience using source control management (CI/CD).
- Proficient in designing relevant architecture / microservices to fulfil application integration, model monitoring, training / re-training, model management, model deployment, model experimentation/development, alert mechanisms.
- Experience with public cloud platforms (Azure, AWS, GCP).
- Serverless services like lambda, azure functions, and/or cloud functions.
- Orchestration services like data factory, data pipeline, and/or data flow.
- Data science workbench/managed services like azure machine learning, sagemaker, and/or AI platform.
- Data warehouse services like snowflake, redshift, bigquery, azure sql dw, AWS Redshift.
- Distributed computing services like Pyspark, EMR, Databricks.
- Data storage services like cloud storage, S3, blob, S3 Glacier.
- Data visualization tools like Power BI, Tableau, Quicksight, and/or Qlik.
- Proven experience serving up predictive algorithms and analytics through batch and real-time APIs.
- Solid working experience with software engineers, data scientists, product owners, business analysts, project managers, and business stakeholders to design the holistic solution.
- Strong technical acumen around automated testing.
- Extensive background in statistical analysis and modeling (distributions, hypothesis testing, probability theory, etc.)
- Strong hands-on experience with statistical packages and ML libraries (e.g., Python scikit learn, Spark MLlib, etc.)
- Experience in effective data exploration and visualization (e.g., Excel, Power BI, Tableau, Qlik, etc.)
- Experience in developing and debugging in one or more of the languages Java, Python.
- Ability to work in cross functional teams.
- Apply Machine Learning techniques in production including, but not limited to, neuralnets, regression, decision trees, random forests, ensembles, SVM, Bayesian models, K-Means, etc.

Roles and Responsibilities:

Deploying ML models into production, and scaling them to serve millions of customers.

Technical solutioning skills with deep understanding of technical API integrations, AI / Data Science, BigData and public cloud architectures / deployments in a SaaS environment.

Strong stakeholder relationship management skills - able to influence and manage the expectations of senior executives.
Strong networking skills with the ability to build and maintain strong relationships with both business, operations and technology teams internally and externally.

Provide software design and programming support to projects.

 Qualifications & Experience:

Engineering and post graduate candidates, preferably in Computer Science, from premier institutions with proven work experience as a Machine Learning Architect (Deployments) or a similar role for 5-7 years.

•  We are looking for a Data Engineer with 3-5 years experience in Python, SQL, AWS (EC2, S3, Elastic Beanstalk, API Gateway), and Java.
• The applicant must be able to perform Data Mapping (data type conversion, schema harmonization) using Python, SQL, and Java.
• The applicant must be familiar with and have programmed ETL interfaces (OAUTH, REST API, ODBC) using the same languages.
• The company is looking for someone who shows an eagerness to learn and who asks concise questions when communicating with teammates.