Data Scientist

What is the purpose of this role

This role is expected to apply strong expertise in data science and machine learning through leading data collection mining, data analysis, data visualizations, and predictive/prescriptive modeling efforts to build, test, and maintain analytics solutions which meet key business needs. This role will work with an analytics translator, other data scientists, and key business partners to define the business problem in a way that can be solved analytically.

Why is this exciting

You will lead the development of efficient and accurate analytical models (ranging from descriptive to prescriptive) and incorporate them into data products or tools which meaningfully make an impact on the business. So if you are an experienced Data Scientist passionate about collaborating with cross-functional teams, tackling real-world problems through testing and learning, have an intellectual curiosity to understand and answer questions using data, and are seeking a work environment that fuels learning, all while making a significant impact in oral care, we strongly encourage you to apply.

What would be the responsibilities for this role

Lead multifaceted analytic studies against large and small volumes of data for a variety of problems including projections, classifications, clustering, pattern analysis, simulations and more with junior data scientists
Co-develop an analytics roadmap for the business domain to which you are assigned
Interpret and analyze (structured and unstructured) data using exploratory statistical and mathematical techniques to identify trends, anomalies, and quantify business results
Integrate data science work into user-facing applications and tools with data engineers, business leads, analysts and developers
Build, deploy, maintain, and refresh end-to-end, repeatable, interpretable, dynamic and scalable models that are seamlessly incorporated into analytic data products
Identify and/or create new data as needed for use cases with limited data
Work with data engineers to clean, massage, and organize data and to define data assets, eg, data models
Lead the creation of analytical (e.g., code libraries ML architecture) and data (e.g., external or partnership based data) assets
Where needed, generate experiments on data using the scientific method, story-tell experimental results, and provide previously undiscovered solutions to data challenges
Be the expert in the room on industry and field trends to push analytic builds towards best practices and state-of-art, as needed
Effectively communicate the analytics approach and how it will meet and address objectives to business partners
Explain principles and approaches to business partners and senior management in a non-technical manner

What does the ideal candidate profile look like

Required:
Atleast 3yrs+ work experience in any FMCG / CPG oragnisation
Academics: Bachelors Degree in Data Science, Mathematics, Computer Science, Engineering, Statistics, Econometrics or other related fields
Minimum 6+ years' experience in programming Languages - Python, R

Preferred

Academics: PhD/Masters
Use case experience with demonstrated success in the sales analytics/ eCommerce domain

What are the functional competencies required for this role

SQL implementations (PostgreSQL, MySQL, Oracle, DB2)
Proven experience in translating business challenges into data pipelines model framework
Demonstrated expertise with supervised and unsupervised modeling - including regressions, classifications, feature selection, clustering, decision tree/forest learning, SVM, ensembling stacking, dimensionality reduction and more
Fundamental understanding of Bayesian statistics and causal inference
Strong proficiency in NLP (natural language processing)
Expertise with data visualization techniques tools such as Plotly, matplotlib, ggplot2, etc, Shiny, Dash, Tableau, etc
Demonstrated experience with large datasets and distributed computing (eg Spark)
Demonstrated experience with statistical tools or packages (eg SciKit) and ML frameworks (eg TensorFlow / PyTorch)
Proficiency or familiarity with relevant database/data engineering needs including:

Real-time data processing frameworks
Metadata systems
Data pipeline monitoring systems
SQL implementations (PostgreSQL, MySQL, Oracle, DB2)
ETL/ELT techniques, especially on cloud infrastructure (eg Google Cloud Platform)

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Data Scientist

Job Description

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