IT​/Tech Associate, Data Scientist

Location: Vasanthanagar

TITLE:

Associate Data Scientist
DEPARTMENT:  Data Sciences & Analytics

LOCATION:

Bangalore, India

Team:
The Center of Excellence on Data Sciences & Analytics is an upstart CoE team, incubated within Victoria’ Secret & CO. (VS&Co), building the best-in-class suite of Data Science & Analytics Products that power the best shopping experience for our customers, and deliver actionable, customer-centric insights to our ecommerce, marketing, merchandising and supply chain teams enabling better business decisions on omni channel (digital + store) 360 performances.

Purpose:

The associate data scientist will work with a senior data scientist and play a key technical role in analysing large data sets to develop custom models and algorithms to drive business solutions:

• You would be building machine learning models on any of the below areas:
1.A/B testing, measurement frameworks & Causal modelling,
2.NLP, LLMs & embeddings, GenAI chat-bots,
3.time-series forecasting & anomaly detection,
4.optimization and recommendation systems,
5.Classification and regression models.

• You have a deep interest and passion for technology. You love writing and owning codes and enjoy working with people who will keep challenging you at every stage. You have strong problem solving, analytic, decision-making, and excellent communication with interpersonal skills. You are self-driven and motivated with the desire to work in a fast-paced, results-driven agile environment with varied responsibilities.

• You will work directly on the AI problems that have the most impact on VS&Co's digital e-commerce, omni-channel, entire supply chain, forecasting and merchandising.

Responsibilities:
An Associate Data Scientist will work very closely with a data science mentor and would be responsible for building data science and machine learning solutions for our business problems across e-commerce, finance, store operations, logistics and supply chain:

• Research and propose innovative statistical/ML models to address the requirements.

• Target and formulate various problems in terms of key business metrics and measure the impact of the built ML techniques.

• Analyze data for trends & patterns and interpret data with a clear objective in mind.

• Develop, deploy ML/DL models in production or help develop a platform to facilitate the same.

• Work closely with the Engineering team to take Data Science projects to production.

• Communicate progress/findings to relevant stakeholders.

• Keep updated with emerging technologies in AI/ML.

Education:

• B.S. degree in Computer Science, Mathematics, Statistics

Skills/

Experience:

• Knowledge of the foundations of machine learning and statistics. Familiarity with statistical methods such as hypothesis testing, forecasting, time series analysis, etc. (Required).

• Experience with SQL, Python (Required).

Experience with PySpark, Snowflake and Rest/Fast APIs (Preferred)

• Experience in Machine Learning, Deep Learning or Engineering aspects of ML model deployment (Required).

• Has or pursuing Bachelor’s/Master’s/PhD degree in Computer Science, Statistics, Applied Mathematics, or related discipline (Preferred)

• Experience of working on optimization problems - linear/nonlinear programming, (meta) heuristics for solving optimization problems (Preferred)

• Proficiency in data analysis, mathematics/probability, and statistical analysis (Required). Past Data Science project experience (Preferred)

• Should be comfortable multitasking and working in a dynamic, fast paced environment.

• Effective communication of DS requirements and dissemination of right knowledge to relevant stakeholders

• Most importantly, an inquisitive mind, an ability for self-learning and abstraction along with a risk appetite for experimentation and failure.

Mandatory

Skills:

Machine Learning, Python, Big Data Skills, R, Statistics

Additional Qualifications:

• Good to have projects experience in any of the below areas:
i.customer analytics, market-mix models, cross-selling/upselling models,
ii.

LLMs, NLP systems, chat-bots
iii.causal models, measurement frameworks, A/B testing
iv.recommendation systems
v.supply chain planning systems
vi.

ML engineering, MLOps practices, experience designing algorithms at scale and end-to-end model-development Life cycles