Data Scientist

JOB SUMMARY : Indicate why the job exists and the overall function or job purpose.

The Lead Data Scientist will deliver on activities related to the strategy, design, development, implementation of Data Science, Artificial Intelligence (AI) and Machine Learning (ML) for enterprise application functions. Frequently interacting with key stakeholders, the Data Scientist will lead and create advanced analytics models from conceptualization to completion. This position will support different products or lines of businesses to build innovations around AI.

DETAILED DESCRIPTION :

List the primary duties and responsibilities required to perform this job in order of importance; typically 4 to 6 key responsibilities.

- Collaborate with business functions/stakeholders on discovery for data science idea generation, provide ideas and influence as well as drive changes

- End-to-end ownership of work from ideation to production.

- Develop, manage and maintain Machine Learning and Deep Learning infrastructure from pipelines and modelling to machine learning products and decision support.

- Work with the business to understand the problem space, identify the opportunities, and translate our problems into analytical solutions

- Work with other Data Teams and Data Engineers to build upon our data collection, storage, and processing infrastructure

- Develop, distribute, support - standards, best practices, innovative and automation modeling techniques, and procedures

- Manage activities that take place during solution ideation, solution design, solution implementation, optimization, and operation

- Coordinate enhancements and maintenance of information systems to ensure highly reliable and cost-effective operation

JOB REQUIREMENTS :

Identify minimum education, experience, and/or skills/requirements necessary to perform the job.

(Required Education) :

- Masters Degree

- Bachelors Degree - Maths, Statistics, Science, Data Science, Computer Science, Electrical/Computer Engineering

- Associates' Degree (U.S.)/College Diploma (Canada)

- High School Degree or Equivalent

- Licenses/Certificates/Designations - Data Science, AI/ML

- Other

(Preferred Education) :

- Masters Degree - MS in Data Science, AL/ML, Business Analytics

- Bachelors Degree

- Associates' Degree (U.S.)/College Diploma (Canada)

- High School Degree or Equivalent

- Licenses/Certificates/Designations - Data Science, AI/ML

- Other

(Required Experience (Indicate # of Years) & Skills) :

- 5+ years of Digital/Data/Analytics/Statistics experience, including 3+ years in Applied AI/ML and Data Science

- Design and conduct experiments, collect the data necessary to perform statistical hypothesis testing, and create inferences and recommendations

- Hands on experience in building Machine Learning-enabled products or working with large complex data sets

- Hands on working knowledge in ML techniques including classification, clustering, dimension reduction, regression, NLP, time series forecasting, and boosting to build explanatory, predictive, and prescriptive models appropriate for solving different problems

- Hands on experience with distributed data/computing and orchestration tools : Azure Databricks, Spark, MySQL etc.

- Hands on expertise with statistical techniques and their applications in business.

- Hands on coding experience with several languages : Python, JavaScript, Java, R etc.

- Experience with design and development of Generative AI models using state-of-the-art techniques

- Strategic planning experience that adapts to communication between technical and business stakeholders

- Strong verbal and written communication skills, interpersonal skills, and organizational skills

- Strong presentation skills with both technical and non-technical audiences

- Strong team leadership and/or project management experience with multiple simultaneous initiatives

- Skilled in the management of complex systems and decision-making under uncertainty

- Excellent time management skills and strong attention to detail, with experience in adhering to best practices of managing the life cycle of data-intensive ML projects

WORKING ENVIRONMENT :

(Check any that are Relevant)

- General Office - Work is generally performed within an office environment, with standard office equipment. Lighting and temperature are adequate, and there are no hazardous or unpleasant conditions caused by noise, dust, etc.

- Outside Sales - Work is generally performed in an office environment, but employees may need to travel to customer sites or warehouse facilities. Driving may be required for an extended period of time with frequent stops and starts. Can be exposed to outdoor weather conditions.

- Driver - Work is generally performed in moving vehicle. Driving required for an extended period of time with frequent stops and starts. Can be exposed to outdoor weather conditions.

- Warehouse - Work is generally performed in a warehouse environment. The noise level is moderately quiet. Generally well ventilated and well lighted. Warehouse employees may be exposed to all kinds of weather and may be required to wear protective clothing. Operates heavy equipment. Strict safety regulations may be required.

PHYSICAL EXPECTATIONS :

(Check any that are Relevant)

- Work is generally sedentary in nature, but may require standing and walking for up to 10% of the time.

- Work is physically strenuous and workers are required to lift heavy packages up to 50 pounds.

- Work may require excessive bending or stooping.

- Employee required to walk long distances repeatedly throughout the day.

- Employee required to sit and drive a vehicle to perform their job.

- Employee required to climb ladders.

- Employee required to use hand tools.

- Employee uses heavy machinery (e.g., forklift, etc.).