Assistant Manager Data Analytics jobs in Noida at The Smart Cube

Assistant Manager – Data Analytics

Noida - India

Roles and responsibilities

Specifically, Assistant Managers should:

Understand client objectives, and work with the Project Lead (PL) to design the analytical solution/framework. Be able to translate client objectives/analytical plan into clear deliverables with associated priorities and constraints
Organise/Prepare/Manage data and conduct quality checks to ensure that the analysis dataset is ready
Explore and implement various statistical and analytical techniques (including machine learning) like linear/non-linear Regression, Decision Trees, Segmentation, time series forecasting, as well as machine learning algorithms – such as Random Forest, SVM and ANN.
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 junior analysts to ensure an error-free output
Interpret the output in context of the client’s business and industry to identify trends and actionable insights
Be able to succinctly visualise the findings through a PPT, a BI dashboard (Tableau, Qlikview, etc.) and highlight the key takeaways from a business perspective
Be able to take client calls relatively independently, and interact with onsite leads (if applicable) on a daily basis
Discuss queries/certain sections of deliverable report over client calls or video conferences

Ideal Candidate :

4–6 years of relevant advanced analytics experience in Marketing, CRM, Pricing in either Retail, or CPG industries. Other B2C domains can be considered
Experience in managing, cleaning and analysing large datasets using tools such as Python, R and SAS
Experience in using multiple advanced analytics techniques or machine learning algorithms
Experience in handling client calls and working independently with clients
Understanding of consumer businesses such as Retail, CPG and Telecom
Knowledge of working across multiple data types and files including flat files and RDBMS files; multiple data platforms (SQL Server, Teradata, Hadoop, Spark); on premise or on the cloud
Knowledge of advanced statistical techniques such as Decision trees, different types of regressions, clustering, Forecasting (ARIMA/X) and ML

Education :

Engineers from top tier institutes (IITs, DCE/NSIT, NITs) or Post Graduates in Maths/Statistics/OR from top Tier Colleges/Universities
MBA from top tier B-schools

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