Analytics Data Engineer

Mandatory Skills: Azure Data Lake Storage, Azure SQL databases, Azure Synapse, Data Bricks (Pyspark/Spark), Python, SQL, Azure Data Factory.

Good to have: Power BI, Azure IAAS services, Azure Devops, Microsoft Fabric

Ø Very strong understanding on ETL and ELT

Ø Very strong understanding on Lakehouse architecture.

Ø Very strong knowledge in Pyspark and Spark architecture.

Ø Good knowledge in Azure data lake architecture and access controls

Ø Good knowledge in Microsoft Fabric architecture

Ø Good knowledge in Azure SQL databases

Ø Good knowledge in T-SQL

Ø Good knowledge in CI /CD process using Azure devops

Ø Power BI

Responsibilities

> Selecting features, building and optimizing classifiers using machine

> learning techniques

> Data mining using state-of-the-art methods

> Extending company’s data with third party sources of information when

> needed

> 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

Key Skills

> Hands-on experience of analysis tools like R, Advance Python

> Must Have Knowledge of statistical techniques and machine learning

> algorithms

> Artificial Intelligence

> Understanding of Text analysis- Natural Language processing (NLP)

> Knowledge on Google Cloud Platform

> Advanced Excel, PowerPoint skills

> Advanced communication (written and oral) and strong interpersonal

> skills

> Ability to work cross-culturally

> Good to have Deep Learning

> VBA and visualization tools like Tableau, PowerBI, Qliksense, Qlikview

> will be an added advantage

Job Summary

As a Data Science Lead, you will manage multiple consulting projects of varying complexity and ensure on-time and on-budget delivery for clients. You will lead a team of data scientists and collaborate across cross-functional groups, while contributing to new business development, supporting strategic business decisions and maintaining & strengthening client base

1. Work with team to define business requirements, come up with analytical solution and deliver the solution with specific focus on Big Picture to drive robustness of the solution
2. Work with teams of smart collaborators. Be responsible for their appraisals and career development.
3. Participate and lead executive presentations with client leadership stakeholders.
4. Be part of an inclusive and open environment. A culture where making mistakes and learning from them is part of life
5. See how your work contributes to building an organization and be able to drive Org level initiatives that will challenge and grow your capabilities.

​​​​​​Role & Responsibilities

1. Serve as expert in Data Science, build framework to develop Production level DS/AI models.
2. Apply AI research and ML models to accelerate business innovation and solve impactful business problems for our clients.
3. Lead multiple teams across clients ensuring quality and timely outcomes on all projects.
4. Lead and manage the onsite-offshore relation, at the same time adding value to the client.
5. Partner with business and technical stakeholders to translate challenging business problems into state-of-the-art data science solutions.
6. Build a winning team focused on client success. Help team members build lasting career in data science and create a constant learning/development environment.
7. Present results, insights, and recommendations to senior management with an emphasis on the business impact.
8. Build engaging rapport with client leadership through relevant conversations and genuine business recommendations that impact the growth and profitability of the organization.
9. Lead or contribute to org level initiatives to build the Tredence of tomorrow.

Qualification & Experience

1. Bachelor's /Master's /PhD degree in a quantitative field (CS, Machine learning, Mathematics, Statistics, Data Science) or equivalent experience.
2. 6-10+ years of experience in data science, building hands-on ML models
3. Expertise in ML – Regression, Classification, Clustering, Time Series Modeling, Graph Network, Recommender System, Bayesian modeling, Deep learning, Computer Vision, NLP/NLU, Reinforcement learning, Federated Learning, Meta Learning.
4. Proficient in some or all of the following techniques: Linear & Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Support Vector Machines ANOVA , Principal Component Analysis, Gradient Boosted Trees, ANN, CNN, RNN, Transformers.
5. Knowledge of programming languages SQL, Python/ R, Spark.
6. Expertise in ML frameworks and libraries (TensorFlow, Keras, PyTorch).
7. Experience with cloud computing services (AWS, GCP or Azure)
8. Expert in Statistical Modelling & Algorithms E.g. Hypothesis testing, Sample size estimation, A/B testing
9. Knowledge in Mathematical programming – Linear Programming, Mixed Integer Programming etc , Stochastic Modelling – Markov chains, Monte Carlo, Stochastic Simulation, Queuing Models.
10. Experience with Optimization Solvers (Gurobi, Cplex) and Algebraic programming Languages(PulP)
11. Knowledge in GPU code optimization, Spark MLlib Optimization.
12. Familiarity to deploy and monitor ML models in production, delivering data products to end-users.
13. Experience with ML CI/CD pipelines.

• Key responsibility is to design & develop a data pipeline for real-time data integration, processing, executing of the model (if required), and exposing output via MQ / API / No-SQL DB for consumption
• Provide technical expertise to design efficient data ingestion solutions to store & process unstructured data, such as Documents, audio, images, weblogs, etc
• Developing API services to provide data as a service
• Prototyping Solutions for complex data processing problems using AWS cloud-native solutions
• Implementing automated Audit & Quality assurance Checks in Data Pipeline
• Document & maintain data lineage from various sources to enable data governance
• Coordination with BIU, IT, and other stakeholders to provide best-in-class data pipeline solutions, exposing data via APIs, loading in down streams, No-SQL Databases, etc

Skills

• Programming experience using Python & SQL
• Extensive working experience in Data Engineering projects, using AWS Kinesys,  AWS S3, DynamoDB, EMR, Lambda, Athena, etc for event processing
• Experience & expertise in implementing complex data pipeline
• Strong Familiarity with AWS Toolset for Storage & Processing. Able to recommend the right tools/solutions available to address specific data processing problems
• Hands-on experience in Unstructured (Audio, Image, Documents, Weblogs, etc) Data processing.
• Good analytical skills with the ability to synthesize data to design and deliver meaningful information
• Know-how on any No-SQL DB (DynamoDB, MongoDB, CosmosDB, etc) will be an advantage.
• Ability to understand business functionality, processes, and flows
• Good combination of technical and interpersonal skills with strong written and verbal communication; detail-oriented with the ability to work independently

Functional knowledge

• Real-time Event Processing
• Data Governance & Quality assurance
• Containerized deployment
• Linux
• Unstructured Data Processing
• AWS Toolsets for Storage & Processing
• Data Security

We are currently looking for a Junior Data Scientist to join our growing Data Science team in Panchkula. As a Jr. Data Scientist, you will work closely with the Head of Data Science and a variety of cross-functional teams to identify opportunities to enhance the customer journey, reduce churn, improve user retention, and drive revenue.

• Medium to Expert level proficiency in either R or Python.
• Expert level proficiency in SQL scripting for RDBMS and NoSQL DBs (especially MongoDB)
• Tracking and insights on key metrics around User Journey, User Retention, Churn Modelling and Prediction, etc.
• Medium-to-Highly skilled in data-structures and ML algorithms, with the ability to create efficient solutions to complex problems.
• Experience of working on an end-to-end data science pipeline: problem scoping, data gathering, EDA, modeling, insights, visualizations, monitoring and maintenance.
• Medium-to-Proficient in creating beautiful Tableau dashboards.
• Problem-solving: Ability to break the problem into small parts and apply relevant techniques to drive the required outcomes.
• Intermediate to advanced knowledge of machine learning, probability theory, statistics, and algorithms. You will be required to discuss and use various algorithms and approaches on a daily basis.
• Proficient in at least a few of the following: regression, Bayesian methods, tree-based learners, SVM, RF, XGBOOST, time series modelling, GLM, GLMM, clustering, Deep learning etc.

• Experience in one of the upcoming technologies like deep learning, recommender systems, etc.
• Experience of working in the Gaming domain
• Marketing analytics, cross-sell, up-sell, campaign analytics, fraud detection
• Experience in building and maintaining Data Warehouses in AWS would be a big plus!

• PF and gratuity
• Working 5 days a week
• Paid leaves (CL, SL, EL, ML) and holidays
• Parties, festivals, birthday celebrations, etc
• Equability: absence of favouritism in hiring & promotion

• 1-5 years of experience in building and maintaining robust data pipelines, enriching data, low-latency/highly-performance data analytics applications.
• Experience handling complex, high volume, multi-dimensional data and architecting data products in streaming, serverless, and microservices-based Architecture and platform.
• Experience in Data warehousing, Data modeling, and Data architecture.
• Expert level proficiency with the relational and NoSQL databases.
• Expert level proficiency in Python, and PySpark.
• Familiarity with Big Data technologies and utilities (Spark, Hive, Kafka, Airflow).
• Familiarity with cloud services (preferable AWS)
• Familiarity with MLOps processes such as data labeling, model deployment, data-model feedback loop, data drift.

Key Roles/Responsibilities:

• Act as a technical leader for resolving problems, with both technical and non-technical audiences.
• Identifying and solving issues with data pipelines regarding consistency, integrity, and completeness.
• Lead data initiatives, architecture design discussions, and implementation of next-generation BI solutions.
• Partner with data scientists, tech architects to build advanced, scalable, efficient self-service BI infrastructure.
• Provide thought leadership and mentor data engineers in information presentation and delivery.

We are actively seeking a Senior Data Engineer experienced in building data pipelines and integrations from 3rd party data sources by writing custom automated ETL jobs using Python. The role will work in partnership with other members of the Business Analytics team to support the development and implementation of new and existing data warehouse solutions for our clients. This includes designing database import/export processes used to generate client data warehouse deliverables.