Data Science Analyst

Key Skills

Analytical and Problem solving, Apache Hive, Apache Pig, Azure, Continuous Integration & Continuous Delivery - CI/CD, Data Analysis, Data science techniques, DevOps, Effective communication skills, Github, Kubeflow, Machine learning techniques, MLflow, MLOps tools, Python Programming, R Programming, SPARK Programming, SQL, Statistical modeling

Job Description

Overview

Job Title - Data Science - Analyst

Main Purpose:

The role will work in developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Specific scope of this role is to develop ML solution in support of ML/AI projects using big analytics toolsets in a CI/CD environment. Analytics toolsets may include DS tools/Spark/Databricks, and other technologies offered by Microsoft Azure or open-source toolsets. This role will also help automate the end-to-end cycle with Azure Machine Learning Services and Pipelines.

Responsibilities

Key Accountabilities:

• Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope
• Collaborate with data engineers and ML engineers to understand data and models and leverage various advanced analytics capabilities
• Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards
• Use big data technologies to help process data and build scaled data pipelines (batch to real time)
• Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines leveraging the NGAA platform (Azure)
• Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure
• Automate ML models deployments

Qualifications

Qualifications:

• 4 – 7 years of overall experience that includes at least 4+ years of hands-on work experience data science / Machine learning
• Minimum 4+ year of SQL experience
• Experience in DevOps and Machine Learning (ML) with hands-on experience with one or more cloud service providers (Azure preferred) is preferred
• BE/BS in Computer Science, Math, Physics, or other technical fields.

Skills, Abilities, Knowledge:

• Data Science – Hands on experience and strong knowledge of building machine learning models – supervised and unsupervised models
• Programming Skills – Hands-on experience in statistical programming languages like Python, R and database query languages like SQL
• Statistics – Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators
• Cloud (Azure) – Experience in Databricks and ADF is desirable

• Familiarity with Spark, Hive, Pig is an added advantage
• Model deployment experience will be a plus
• Experience with version control systems like GitHub and CI/CD tools
• Experience is Exploratory data Analysis
• Knowledge of ML Ops / DevOps and deploying ML models is required

• Experience using MLFlow, Kubeflow etc. will be preferred
• Experience executing and contributing to ML OPS automation infrastructure is good to have
• Exceptional analytical and problem-solving skills

Differentiating Competencies Required

• Ability to work with virtual teams (remote work locations); lead team of technical resources (employees and contractors) based in multiple locations across geographies
• Lead technical discussions, driving clarity of complex issues/requirements to build robust solutions
• Strong communication skills to meet with business, understand sometimes ambiguous, needs, and translate to clear, aligned requirements
• Able to work independently with business partners to understand requirements quickly, perform analysis and lead the design review sessions
• Highly influential and having the ability to educate challenging stakeholders on the role of data and its purpose in the business
• Places the user in the centre of decision making
• Teams up and collaborates for speed, agility, and innovation
• Experience with and embraces agile methodologies
• Strong negotiation and decision-making skill
• Experience managing and working with globally distributed teams