Senior Data Scientist Job Description Template
Our company is looking for a Senior Data Scientist to join our team.
Responsibilities:
- Manage the delivery, quality and integrity of analytics services/projects in line with agreed roadmap of planned and ad hoc briefs;
- Proactive & reactive problem evaluation of opportunities to improve current process/outcomes;
- Production of complex ad hoc analysis where required;
- Participate in regular project updates;
- Analyze large amounts of information to discover trends and insights;
- Supervise all analytics development phases, including testing, documentation and on-going tracking;
- Communicate complex analysis and insights (oral and written);
- Build predictive models and machine-learning algorithms;
- Analyze business problems, develop solutions to these problems and manage analytics projects through its full cycle;
- Work independently with minimal support and supervision;
- Work simultaneously on a number of different projects of varying complexity and length;
- Supervising junior analytical staff;
- Optimize joint development efforts through appropriate database use and project design;
- Data mining;
- Processing, cleansing and verifying the integrity of data used for analysis.
Requirements:
- Extensive background in Data Mining and Statistical Analysis;
- Some understanding of the Agile methodology, be a self-starter, willing to work flexibly and long hours, results driven and focussed;
- Excellent Pattern Recognition and Predictive Modelling skills: 3-4 years of experience;
- Able to understand various data structures and common methods in data transformation- coupled with 3-4 years of working experience;
- Experience with Python, R, NumPy etc;
- Understanding and experience of machine learning techniques and algorithms, such as k-NN, Naïve Bayes, SVM, Decision Forests, etc;
- 7+ years practical experience with SAS, ETL, Data Processing, Database Programming and Data Analytics. SAS is a non-negotiable;
- Experience in Research Methodologies: Scientific Reasoning;
- Data Orientated personality;
- Good applied Statistics skills, such as distributions, Statistical Testing, Regression etc;
- Great communication skills;
- Proficiency in using query languages such as SQL, Hive, NoSQL;
- Good Scripting and Programming skills.