The role of a Data Science Manager involves driving sales and solutioning for data science/analytics projects, managing and supervising project delivery, mentoring teams, building new capabilities, and contributing to thought leadership while developing new skills. The ideal candidate should seamlessly collaborate with client teams to position data science/analytical capabilities and work with them to deliver data science/analytics projects.
To succeed in this position, the candidate should possess strong consultative, business, and communication skills in addition to quantitative abilities. They should also be familiar with the challenges of using statistics in a business setting, including incomplete or biased data, large data sets, low signal-to-noise ratios, high variance, and multiple objective functions. Creativity and resourcefulness are essential, and the candidate must be proficient in traditional statistical methodologies as well as newer techniques such as computational statistics, data mining, and big data capabilities.
Responsibilities:
- Address client’s operational challenges by using data and providing new insights from advanced predictive and optimization models.
- Collect data from disparate data sources by interacting with client’s IT team.
- Design models, algorithms, and visualizations that will help companies distil insights from huge volumes of chaotic data.
- Interpret analytical models, conduct analysis to gain insights into the root of clients’ business problem, propose potential solutions to their problems, and assist in quantifying improvements in their operations and supply chain metrics.
- Create high impact business strategies and roadmaps for solution implementation, to help clients reap benefits of data analytics on an ongoing basis.
- Supervise and mentor a team of analysts and consultants to gather data across multiple systems and interpret clients’ problem and convert them into analytical solutions.
- Conduct project and issue management (status reporting, issue reporting, budget reporting) for assigned scope of work, and make decisions that impact the team through regular consultation with senior management.
- Adhere to strategic direction set by senior management. Interact with client and internal senior management on a regular basis.
Qualifications:
BSc, MSc or PhD in Computer Science, Statistics, Mathematics, Engineering, Physics, or related science field from a well-established University
- Minimum of 7+ years of proven working experience in Data Science & ML areas, leading a team of 5-8 people.
- Solid theoretical and hands-on knowledge of Econometrics, Statistics, Data Mining, Machine Learning & Artificial Intelligence concepts.
- Exceptional programming skills in Data Science scripting (such as Python / R / Scala / Julia).
- Exceptional analytical and critical thinking skills with demonstrated ability to think strategically; turn consumer behavior data into effective strategies and drive results.
- Ability to communicate results effectively and explain technical methodologies to non-technical audience.
- Demonstrated ability in the application of Machine Learning to real-world industrial settings with large scale data in the mining, chemical, oil & gas, finance or retail industries.
- Ability to work as a team player in multinational project teams.
- Considered a plus:
- Experience with Cloud Technologies (MS Azure, GCP, AWS)
- Familiarity with Deep Learning concepts & tools (H2O, TensorFlow, Py Torch, Keras, Theano, etc.)
- Understanding of ML lifecycle and hands-on experience with ML Ops
- Experience in SQL and interpretation of large databases (via tools such as Hive, Spark, HBASE, HDFS, Kafka, Kudu would be an asset)
- Visualization tools (Power BI, Tableau)
- Working under an Agile Framework with CICD principles
