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Statistical Modelling jobs

Statistical Modelling 


Statistical Modelling is where data from multiple sources is drawn together and scrutinized in depth to pull together key insights into behaviors or trends that can be utilized by businesses to aid growth.
The need for Statistical Modelling is vital as it is both descriptive and predictive. A strong statistical analyst will be able to understand why customers do the things they do and predict what they will do next, to ensure that business and marketing spend is allocated accordingly to increase ROI and business growth. 

Defining Focus


Finding the right match of candidate and client requires an insight that we at Harnham know is critical to nurturing an existing customer base and for new business acquisition.
We can fine tune the search and help create the right partnerships. Call us to find out how on 212-796-6070 or email info@harnham.com 

Latest Jobs

Senior Data Scientist
Salary

US$150000 - US$175000 per year

Location

New York

Description

Do you understand why customers buy what they buy and what motivates them to spend their money?

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Senior Analyst- Advanced Analytics
Salary

US$90000 - US$105000 per year

Location

New York

Description

Senior Analyst- Advanced Analytics role! If you are interested in building your career within a successful healthcare tech company, this role is for you!

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Advanced Analytics Lead
Salary

US$115000 - US$130000 per year + Equity

Location

Boston, Massachusetts

Description

Advanced Analytics Lead position based out of the Boston area! SQL/Python/R skills are required.

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Senior User Research Manager - Advanced Analytics
Salary

US$175000 - US$200000 per year

Location

Boston, Massachusetts

Description

Are you a User Research expert with strong technical skills in Python, R and SQL? If so and you want to join a global company, then get in touch!

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Senior User Research Manager - Advanced Analytics
Salary

US$175000 - US$200000 per year

Location

New York

Description

Are you a User Research expert with strong technical skills in Python, R and SQL? If so and you want to join a global company, then get in touch!

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Harnham blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out our recent posts below.

Common Mistakes About Data Science And Your Role Within It

Earlier this month I had the pleasure of speaking at ODSC East, as the best future talent Data Science talent gathered together to discuss the direction of our industry.  With Data Science becoming such a broad term that covers a number of functions, and with the rise of new areas such as Blockchain, AI and ML, I wanted to talk about what it actually means to be a Data Scientist now, and in the future.  With this in mind, we conducted a survey over the course of the event where we asked what Data Science meant to the people there. Here’s what we found out: WHAT IS DATA SCIENTIST, ACTUALLY? Every company thinks they need one, and every analyst wants to be one, but more and more job titles that are not necessarily Data Science are now being billed as Data Scientists. In fact, when we asked people what they considered their job title to be, regardless of experience, Data Science came out on top: Data Scientist: 58% Data Analyst: 22% Machine Learning Engineer: 10% Business Intelligence Analyst: 9%  However, from my experience, this is not necessarily accurate. I once worked with the Senior Manager of Data Science in a very well established Retailer. He’d been there for less than one year and was already on the job market. In his interview he had been told that the company were fully behind investing in a top-class Data Science department but had actually ended up managing a team of people who were building dashboards creating reports for all areas of the business. This is much less Data Science, and much more Business Intelligence.  This confusion is quite typical within the industry and frequently needs to both unhappy employers and employees.  MORE THAN JUST TOOLS One common mistake when it comes to misidentifying Data Scientists is a result of people focusing on the tools people use. Whilst both Data Scientists and Marketing & Insight specialists might be skilled up in Python, R and SQL, their methodologies are significantly different. When asked to define a true Data Scientist at the event, 73% of people agreed the definition is: “A person who uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.” Companies who panic about needing a Data Scientist to keep up with their competitors often ignore these crucial points and end up listing  every tool on a job spec. Frequently those who claim they want a Data Scientist actually want an Insight Analyst who can understand how customers behave, what they respond well to, what they’re talking about on social media, and how this unstructured data can be used to help their business make better decisions. WHAT DOES THIS MEAN FOR ME? For someone wanting to work in the Data & Analytics field there is one key rule: Know Yourself.  Think carefully about aspects within your Science, Operational Research, Statistics, and Analytics in general that you enjoy and how you can work them into your career. If you’re in college and just starting your career, don’t limit yourself by the sectors you think you have to work in; enjoy gaming? The gaming industry uses Data to make characters more lifelike, make sure they move in real-time and ensure that they play in a realistic way.  Just as crucial, however, is having an understanding of what the analytical teams around you do. Consider what roles they play in your business and how you are all interlinked, whilst being aware of the unique differences between roles. And, outside of analytics, those who understand what impact their work has on a business will always stand out amongst a crowd.  Essentially, don’t let yourself be limited by the title of Data Scientist. There are hundreds of roles within Data & Analytics so think about which one is right for you, rather than following the crowd.  If you’re looking for your next opportunity in Data & Analytics, or are looking to build out a team, take a look at our latest roles or get in touch with one of our expert consultants: For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.  Or, if you'd like to talk to me directly about anything I've talked about above, feel free to drop me a line at jennikavanagh@harnham.com.

Jenni Kavanagh

Jenni Kavanagh

30. May 2019

Finance, Risk & Pricing: The Actuarial Analyst

Finance, Risk & Pricing: The Actuarial Analyst

Just because pricing deals with numbers, it doesn’t mean it’s exclusive to the financial sector. In our last few posts, we focused heavily on the role of Pricing Analyst, what it is and how to get there. This type of analyst role is more often found in the marketing arm of many companies and might also be known as Behavior Analyst, Customer Analyst, or something similar. However, there is another type of analyst sometimes confused with Pricing Analyst which falls squarely within the Finance sector. These roles might boast titles such as Risk Analyst, Financial Analyst, or Actuary. Often, it isn’t the title that speaks to the particular strengths of one type of role over another; it is the responsibilities and skill sets documented within the job description. Like Pricing Analysts, these professionals deal with numbers and pricing. However, their focus is on models, such as those required for mergers and acquisitions or how to set health insurance premiums looking at risk.  Looking for a Low to No-Risk Gig? Actuaries are in high demand. As a profession, it is one of the most diverse and tends to be more open to women and under-represented minorities. Though the focus is often on insurance and pension programs, Actuaries can find work in a number of industries including consulting firms, hospitals, banks, investment firms, and government.  As advisors who manage risk portfolios while analyzing historic and current data, these professionals are business-minded people with a mathematical basis. Using mathematics, statistics, and financial theory, they analyze the financial consequences of risk. The Masonic-esque Levels of Becoming an Actuary For individuals who are numbers focused and are interested in using their data, technical, and mathematical skills coupled with business acumen; the role of Actuary might be the perfect fit. However, there are steps or levels which need to follow to enter the profession. These are exam-based and work-experience levels and your salary increase incrementally with each step. To begin, a graduate with a high GPA and one exam under their belt may find the role quite lucrative. Each exam leads to the next level and enters you into an Actuarial Society. Depending on where and what you want to practice will determine which society you’ll sit the exam: Society of Actuaries (SOA) – focus is life and health insurance, pensions, and employee benefits. Casualty Actuarial Society (CAS) – focus is automobile, fire, and liability insurance as well as worker’s compensation. American Society of Pension Actuaries (ASPA) – focus is those in the pension field, particularly in relation to federal and state governments.  Each organization has its own exams and competition is fierce. Qualities sought beyond a high GPA and actuarial exam include: Good communication skills High technical ability A wide background from mathematics and statistics to the liberal arts Actuaries and analysts with an eye toward the financial and insurance sectors use their statistical skills to research, network, and connect the dots between discerned variables. The research begins with statistical modeling. Connect the Dots with Statistical Modeling In statistical forecasting models, the information gathered helps analysts make statements about real outcomes which haven’t yet come to pass. The model can then help identify what might influence these variables. An Actuary, Financial Analyst, or Risk Analyst may use a:  Merger Model (M&A) – This model is most often used in investment banking and corporate development. Think mergers and acquisitions. After all, someone has to decide the value of each company, then the basis of that value once they’re merged. Complexity varies widely in this model. Budget Model – This model is used in financial planning and analysis and helps set the budget for the coming year and the years to come. Focused heavily on a company’s income, these budgets are designed on a monthly or quarterly basis. Forecasting Model – This model is used to build a forecast of the budget model. Think of it as a building block as companies structure their budget and strategies using one or a combination of these models listed. Sometimes, the forecasting and budget model are combined. Sometimes they’re kept separate. These are only three of the ten types of models used in financial planning and analysis for any number of firms and industries. But, it’s the people behind the numbers who help businesses navigate what is best for their client, customer, and bottom line.  An Actuary is just one title those interested in the mathematical and statistical applications for business might find interesting. And like many of those in the Data Science field and higher tech applications, this role is in high demand. Are you the one companies are looking for? If you’re interested in finance, modeling, statistics, Big Data & Analytics, we may have a role for you. We specialize in junior and senior roles. Check out our current vacancies or contact one of our recruitment consultants to learn more.  For our West Coast Team, call (415) 614-4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast Teams, call (212) 796-6070 or send an email to newyorkinfo@harnham.com.

Jenni Kavanagh

Jenni Kavanagh

07. February 2019

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